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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.heatmap(X.corr())" ] }, { "cell_type": "code", "execution_count": 10, "id": "9e095cf4-686a-4478-acfa-827b8162a8f2", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 0)" ] }, { "cell_type": "code", "execution_count": 11, "id": "2903a77a-3087-4607-a311-2834f455512e", "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "classifier = LogisticRegression()" ] }, { "cell_type": "code", "execution_count": 12, "id": "e2a840cb-ac4c-428e-9ac2-12ae710face5", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "parameter = {'penalty' : ['l1','l2','elasticnet'], \n", " 'C':[10,20,30,40,50], \n", " 'max_iter':[100,200,300]}" ] }, { "cell_type": "code", "execution_count": 13, "id": "1946c563-865a-4b10-928f-690c656a65b3", "metadata": {}, "outputs": [], "source": [ "classifier_regressor = GridSearchCV(classifier, \n", " param_grid = parameter, \n", " scoring = 'accuracy', \n", " cv = 5)" ] }, { "cell_type": "code", "execution_count": 14, "id": "813ed530-5363-4788-b2b1-603470902efb", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:378: FitFailedWarning: \n", "150 fits failed out of a total of 225.\n", "The score on these train-test partitions for these parameters will be set to nan.\n", "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", "\n", "Below are more details about the failures:\n", "--------------------------------------------------------------------------------\n", "75 fits failed with the following error:\n", "Traceback (most recent call last):\n", " File \"C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", " File \"C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1162, in fit\n", " solver = _check_solver(self.solver, self.penalty, self.dual)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 54, in _check_solver\n", " raise ValueError(\n", "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n", "\n", "--------------------------------------------------------------------------------\n", "75 fits failed with the following error:\n", "Traceback (most recent call last):\n", " File \"C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", " File \"C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1162, in fit\n", " solver = _check_solver(self.solver, self.penalty, self.dual)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 54, in _check_solver\n", " raise ValueError(\n", "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n", "\n", " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:952: UserWarning: One or more of the test scores are non-finite: [ nan 0.94 nan nan 0.94 nan nan 0.94 nan nan 0.95 nan nan 0.95\n", " nan nan 0.95 nan nan 0.96 nan nan 0.96 nan nan 0.96 nan nan\n", " 0.96 nan nan 0.96 nan nan 0.96 nan nan 0.96 nan nan 0.97 nan\n", " nan 0.97 nan]\n", " warnings.warn(\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "C:\\Users\\kiran\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "data": { "text/html": [ "
GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [10, 20, 30, 40, 50], 'max_iter': [100, 200, 300],\n",
       "                         'penalty': ['l1', 'l2', 'elasticnet']},\n",
       "             scoring='accuracy')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" ], "text/plain": [ "GridSearchCV(cv=5, estimator=LogisticRegression(),\n", " param_grid={'C': [10, 20, 30, 40, 50], 'max_iter': [100, 200, 300],\n", " 'penalty': ['l1', 'l2', 'elasticnet']},\n", " scoring='accuracy')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier_regressor.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 15, "id": "14f66f23-37d7-4eac-8a6d-017ac1c3b327", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier_regressor.get_params" ] }, { "cell_type": "code", "execution_count": 16, "id": "ddd0513e-520c-4b1b-ae06-6ae1b7d7c02f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'C': 50, 'max_iter': 200, 'penalty': 'l2'}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier_regressor.best_params_" ] }, { "cell_type": "code", "execution_count": 17, "id": "ef8d6f70-9a7d-4e95-967b-b02f0a8fdabd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9700000000000001" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier_regressor.best_score_" ] }, { "cell_type": "code", "execution_count": 18, "id": "6657ac0c-9c05-46c1-9a6f-c93b2dd4de0e", "metadata": {}, "outputs": [], "source": [ "y_pred = classifier_regressor.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 19, "id": "aebe7019-7a51-44ef-8cba-54b22197ab80", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['virginica', 'versicolor', 'setosa', 'virginica', 'setosa',\n", " 'virginica', 'setosa', 'versicolor', 'versicolor', 'versicolor',\n", " 'virginica', 'versicolor', 'versicolor', 'versicolor',\n", " 'versicolor', 'setosa', 'versicolor', 'versicolor', 'setosa',\n", " 'setosa', 'virginica', 'versicolor', 'setosa', 'setosa',\n", " 'virginica', 'setosa', 'setosa', 'versicolor', 'versicolor',\n", " 'setosa', 'virginica', 'versicolor', 'setosa', 'virginica',\n", " 'virginica', 'versicolor', 'setosa', 'virginica', 'versicolor',\n", " 'versicolor', 'virginica', 'setosa', 'virginica', 'setosa',\n", " 'setosa', 'versicolor', 'virginica', 'virginica', 'virginica',\n", " 'virginica'], dtype=object)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred" ] }, { "cell_type": "code", "execution_count": 20, "id": "2b088c4e-4a27-45f4-9d66-ef6d971bb5ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The accuracy score is 0.98\n", " precision recall f1-score support\n", "\n", " setosa 1.00 1.00 1.00 16\n", " versicolor 0.95 1.00 0.97 18\n", " virginica 1.00 0.94 0.97 16\n", "\n", " accuracy 0.98 50\n", " macro avg 0.98 0.98 0.98 50\n", "weighted avg 0.98 0.98 0.98 50\n", "\n" ] } ], "source": [ "from sklearn.metrics import accuracy_score, classification_report\n", "print('The accuracy score is ', accuracy_score(y_pred, y_test))\n", "print(classification_report(y_pred, y_test))" ] }, { "cell_type": "code", "execution_count": 23, "id": "1820f68f-e001-4423-bc20-83fcec5392cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "virginica\n" ] } ], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "# sepal_length, sepal_width, petal_length, petal_width\n", "# result = classifier_regressor.predict([[4,2,1,0]]) #setosa\n", "# result = classifier_regressor.predict([[5.8,3,4.3,1.3]]) #versicolor\n", "result = classifier_regressor.predict([[8,4,7,3]]) #virginica\n", "\n", "if result == 'setosa':\n", " print('setosa')\n", "elif result == 'versicolor':\n", " print('versicolor')\n", "elif result == 'virginica':\n", " print('virginica')\n", "else:\n", " print('not found')" ] }, { "cell_type": "code", "execution_count": null, "id": "26ba3c88-495c-49ba-a4d5-102c3114d381", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }