GridSearchCV(cv=5,\n",
+ " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf',\n",
+ " plq_Ridge_Classifier(loss={'name': 'svm'}))]),\n",
+ " param_grid={'clf__C': [0.1, 1.0, 10.0],\n",
+ " 'clf__loss': [{'name': 'svm'}, {'name': 'sSVM'},\n",
+ " {'name': 'squared SVM'}]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5,\n",
+ " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf',\n",
+ " plq_Ridge_Classifier(loss={'name': 'svm'}))]),\n",
+ " param_grid={'clf__C': [0.1, 1.0, 10.0],\n",
+ " 'clf__loss': [{'name': 'svm'}, {'name': 'sSVM'},\n",
+ " {'name': 'squared SVM'}]})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(C=0.1, loss={'name': 'svm'}))])StandardScaler()
plq_Ridge_Classifier(C=0.1, loss={'name': 'svm'})GridSearchCV(cv=5,\n",
+ " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(loss={'name': 'mse'}))]),\n",
+ " param_grid={'reg__C': [0.1, 1.0, 10.0],\n",
+ " 'reg__loss': [{'name': 'huber', 'tau': 1.0},\n",
+ " {'name': 'mae'}, {'name': 'mse'}]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5,\n",
+ " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(loss={'name': 'mse'}))]),\n",
+ " param_grid={'reg__C': [0.1, 1.0, 10.0],\n",
+ " 'reg__loss': [{'name': 'huber', 'tau': 1.0},\n",
+ " {'name': 'mae'}, {'name': 'mse'}]})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg', plq_Ridge_Regressor(loss={'name': 'mae'}))])StandardScaler()
plq_Ridge_Regressor(loss={'name': 'mae'})plq_Ridge_Classifier(C=0.001, loss={'name': 'svm'}, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(C=0.001, loss={'name': 'svm'}, max_iter=10000)plq_Ridge_Classifier(C=0.001,\n",
+ " constraint=[{'decreasing': True, 'name': 'monotonic'}],\n",
+ " loss={'name': 'svm'}, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(C=0.001,\n",
+ " constraint=[{'decreasing': True, 'name': 'monotonic'}],\n",
+ " loss={'name': 'svm'}, max_iter=10000)plq_Ridge_Regressor(fit_intercept=False, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
plq_Ridge_Regressor(fit_intercept=False, max_iter=10000)
plq_Ridge_Regressor(constraint=[{'A': array([[1., 1., 0., 0., 0.]]),\n",
+ " 'b': array([-3.]), 'name': 'custom'}],\n",
+ " fit_intercept=False, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(constraint=[{'A': array([[1., 1., 0., 0., 0.]]),\n",
+ " 'b': array([-3.]), 'name': 'custom'}],\n",
+ " fit_intercept=False, max_iter=10000)plq_Ridge_Classifier(loss={'name': 'svm'}, max_iter=50000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'svm'}, max_iter=50000)plq_Ridge_Classifier(constraint=[{'name': 'fair', 'sen_idx': [0],\n",
+ " 'tol_sen': 0.1}],\n",
+ " loss={'name': 'svm'}, max_iter=50000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(constraint=[{'name': 'fair', 'sen_idx': [0],\n",
+ " 'tol_sen': 0.1}],\n",
+ " loss={'name': 'svm'}, max_iter=50000)plq_Ridge_Regressor(loss={'name': 'mae'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(loss={'name': 'mae'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg', plq_Ridge_Regressor(loss={'name': 'mae'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg', plq_Ridge_Regressor(loss={'name': 'mae'}))])StandardScaler()
plq_Ridge_Regressor(loss={'name': 'mae'})plq_Ridge_Regressor(loss={'name': 'mse'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(loss={'name': 'mse'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Regressor(loss={'name': 'mse'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Regressor(loss={'name': 'mse'}))])StandardScaler()
plq_Ridge_Regressor(loss={'name': 'mse'})plq_Ridge_Regressor(C=0.001, loss={'name': 'QR', 'qt': 0.95})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.001, loss={'name': 'QR', 'qt': 0.95})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(C=0.001,\n",
+ " loss={'name': 'QR', 'qt': 0.95}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(C=0.001,\n",
+ " loss={'name': 'QR', 'qt': 0.95}))])StandardScaler()
plq_Ridge_Regressor(C=0.001, loss={'name': 'QR', 'qt': 0.95})plq_Ridge_Regressor(C=0.005,\n",
+ " loss={'epsilon': 0.1, 'name': 'check_eps', 'qt': 0.9})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.005,\n",
+ " loss={'epsilon': 0.1, 'name': 'check_eps', 'qt': 0.9})plq_Ridge_Regressor(C=0.005,\n",
+ " loss={'epsilon': 0.2, 'name': 'check_eps', 'qt': 0.95})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.005,\n",
+ " loss={'epsilon': 0.2, 'name': 'check_eps', 'qt': 0.95})plq_Ridge_Classifier(loss={'name': 'svm'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'svm'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'svm'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'svm'}))])StandardScaler()
plq_Ridge_Classifier(loss={'name': 'svm'})plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'}))])StandardScaler()
plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'})plq_Ridge_Classifier(loss={'name': 'sSVM'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'sSVM'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'sSVM'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'sSVM'}))])StandardScaler()
plq_Ridge_Classifier(loss={'name': 'sSVM'})plq_Ridge_Classifier(loss={'name': 'squared SVM'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'squared SVM'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'squared SVM'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'squared SVM'}))])StandardScaler()
plq_Ridge_Classifier(loss={'name': 'squared SVM'})plq_Ridge_Regressor(C=0.001, loss={'name': 'huber', 'tau': 5.0})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.001, loss={'name': 'huber', 'tau': 5.0})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(C=0.001,\n",
+ " loss={'name': 'huber', 'tau': 5.0}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(C=0.001,\n",
+ " loss={'name': 'huber', 'tau': 5.0}))])StandardScaler()
plq_Ridge_Regressor(C=0.001, loss={'name': 'huber', 'tau': 5.0})plq_Ridge_Regressor(fit_intercept=False, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
plq_Ridge_Regressor(fit_intercept=False, max_iter=10000)
plq_Ridge_Regressor(constraint=[{'A': array([[1., 1., 0., 0., 0.]]),\n",
+ " 'b': array([-3.]), 'name': 'custom'}],\n",
+ " fit_intercept=False, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(constraint=[{'A': array([[1., 1., 0., 0., 0.]]),\n",
+ " 'b': array([-3.]), 'name': 'custom'}],\n",
+ " fit_intercept=False, max_iter=10000)plq_Ridge_Classifier(loss={'name': 'svm'}, max_iter=50000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'svm'}, max_iter=50000)plq_Ridge_Classifier(constraint=[{'name': 'fair', 'sen_idx': [0],\n",
+ " 'tol_sen': 0.1}],\n",
+ " loss={'name': 'svm'}, max_iter=50000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(constraint=[{'name': 'fair', 'sen_idx': [0],\n",
+ " 'tol_sen': 0.1}],\n",
+ " loss={'name': 'svm'}, max_iter=50000)GridSearchCV(cv=5,\n",
+ " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf',\n",
+ " plq_Ridge_Classifier(loss={'name': 'svm'}))]),\n",
+ " param_grid={'clf__C': [0.1, 1.0, 10.0],\n",
+ " 'clf__loss': [{'name': 'svm'}, {'name': 'sSVM'},\n",
+ " {'name': 'squared SVM'}]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5,\n",
+ " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf',\n",
+ " plq_Ridge_Classifier(loss={'name': 'svm'}))]),\n",
+ " param_grid={'clf__C': [0.1, 1.0, 10.0],\n",
+ " 'clf__loss': [{'name': 'svm'}, {'name': 'sSVM'},\n",
+ " {'name': 'squared SVM'}]})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(C=0.1, loss={'name': 'svm'}))])StandardScaler()
plq_Ridge_Classifier(C=0.1, loss={'name': 'svm'})GridSearchCV(cv=5,\n",
+ " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(loss={'name': 'mse'}))]),\n",
+ " param_grid={'reg__C': [0.1, 1.0, 10.0],\n",
+ " 'reg__loss': [{'name': 'huber', 'tau': 1.0},\n",
+ " {'name': 'mae'}, {'name': 'mse'}]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5,\n",
+ " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(loss={'name': 'mse'}))]),\n",
+ " param_grid={'reg__C': [0.1, 1.0, 10.0],\n",
+ " 'reg__loss': [{'name': 'huber', 'tau': 1.0},\n",
+ " {'name': 'mae'}, {'name': 'mse'}]})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg', plq_Ridge_Regressor(loss={'name': 'mae'}))])StandardScaler()
plq_Ridge_Regressor(loss={'name': 'mae'})plq_Ridge_Regressor(C=0.001, loss={'name': 'huber', 'tau': 5.0})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.001, loss={'name': 'huber', 'tau': 5.0})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(C=0.001,\n",
+ " loss={'name': 'huber', 'tau': 5.0}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(C=0.001,\n",
+ " loss={'name': 'huber', 'tau': 5.0}))])StandardScaler()
plq_Ridge_Regressor(C=0.001, loss={'name': 'huber', 'tau': 5.0})plq_Ridge_Regressor(loss={'name': 'mae'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(loss={'name': 'mae'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg', plq_Ridge_Regressor(loss={'name': 'mae'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg', plq_Ridge_Regressor(loss={'name': 'mae'}))])StandardScaler()
plq_Ridge_Regressor(loss={'name': 'mae'})plq_Ridge_Regressor(loss={'name': 'mse'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(loss={'name': 'mse'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Regressor(loss={'name': 'mse'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Regressor(loss={'name': 'mse'}))])StandardScaler()
plq_Ridge_Regressor(loss={'name': 'mse'})plq_Ridge_Classifier(C=0.001, loss={'name': 'svm'}, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(C=0.001, loss={'name': 'svm'}, max_iter=10000)plq_Ridge_Classifier(C=0.001,\n",
+ " constraint=[{'decreasing': True, 'name': 'monotonic'}],\n",
+ " loss={'name': 'svm'}, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(C=0.001,\n",
+ " constraint=[{'decreasing': True, 'name': 'monotonic'}],\n",
+ " loss={'name': 'svm'}, max_iter=10000)plq_Ridge_Regressor(C=0.005,\n",
+ " loss={'epsilon': 0.1, 'name': 'check_eps', 'qt': 0.9})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.005,\n",
+ " loss={'epsilon': 0.1, 'name': 'check_eps', 'qt': 0.9})plq_Ridge_Regressor(C=0.005,\n",
+ " loss={'epsilon': 0.2, 'name': 'check_eps', 'qt': 0.95})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.005,\n",
+ " loss={'epsilon': 0.2, 'name': 'check_eps', 'qt': 0.95})plq_Ridge_Regressor(C=0.001, loss={'name': 'QR', 'qt': 0.95})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.001, loss={'name': 'QR', 'qt': 0.95})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(C=0.001,\n",
+ " loss={'name': 'QR', 'qt': 0.95}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(C=0.001,\n",
+ " loss={'name': 'QR', 'qt': 0.95}))])StandardScaler()
plq_Ridge_Regressor(C=0.001, loss={'name': 'QR', 'qt': 0.95})plq_Ridge_Classifier(loss={'name': 'svm'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'svm'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'svm'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'svm'}))])StandardScaler()
plq_Ridge_Classifier(loss={'name': 'svm'})plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('reg',\n",
+ " plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'}))])StandardScaler()
plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'})plq_Ridge_Classifier(loss={'name': 'sSVM'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'sSVM'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'sSVM'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'sSVM'}))])StandardScaler()
plq_Ridge_Classifier(loss={'name': 'sSVM'})plq_Ridge_Classifier(loss={'name': 'squared SVM'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'squared SVM'})Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'squared SVM'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
+ " ('clf', plq_Ridge_Classifier(loss={'name': 'squared SVM'}))])StandardScaler()
plq_Ridge_Classifier(loss={'name': 'squared SVM'})GridSearchCV(cv=5,\n",
- " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf',\n",
- " plq_Ridge_Classifier(loss={'name': 'svm'}))]),\n",
- " param_grid={'clf__C': [0.1, 1.0, 10.0],\n",
- " 'clf__loss': [{'name': 'svm'}, {'name': 'sSVM'},\n",
- " {'name': 'squared SVM'}]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5,\n",
- " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf',\n",
- " plq_Ridge_Classifier(loss={'name': 'svm'}))]),\n",
- " param_grid={'clf__C': [0.1, 1.0, 10.0],\n",
- " 'clf__loss': [{'name': 'svm'}, {'name': 'sSVM'},\n",
- " {'name': 'squared SVM'}]})Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf', plq_Ridge_Classifier(C=0.1, loss={'name': 'svm'}))])StandardScaler()
plq_Ridge_Classifier(C=0.1, loss={'name': 'svm'})GridSearchCV(cv=5,\n",
- " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg',\n",
- " plq_Ridge_Regressor(loss={'name': 'mse'}))]),\n",
- " param_grid={'reg__C': [0.1, 1.0, 10.0],\n",
- " 'reg__loss': [{'name': 'huber', 'tau': 1.0},\n",
- " {'name': 'mae'}, {'name': 'mse'}]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5,\n",
- " estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg',\n",
- " plq_Ridge_Regressor(loss={'name': 'mse'}))]),\n",
- " param_grid={'reg__C': [0.1, 1.0, 10.0],\n",
- " 'reg__loss': [{'name': 'huber', 'tau': 1.0},\n",
- " {'name': 'mae'}, {'name': 'mse'}]})Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg', plq_Ridge_Regressor(loss={'name': 'mae'}))])StandardScaler()
plq_Ridge_Regressor(loss={'name': 'mae'})plq_Ridge_Classifier(C=0.001, loss={'name': 'svm'}, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(C=0.001, loss={'name': 'svm'}, max_iter=10000)plq_Ridge_Classifier(C=0.001,\n",
- " constraint=[{'decreasing': True, 'name': 'monotonic'}],\n",
- " loss={'name': 'svm'}, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(C=0.001,\n",
- " constraint=[{'decreasing': True, 'name': 'monotonic'}],\n",
- " loss={'name': 'svm'}, max_iter=10000)plq_Ridge_Regressor(fit_intercept=False, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
plq_Ridge_Regressor(fit_intercept=False, max_iter=10000)
plq_Ridge_Regressor(constraint=[{'A': array([[1., 1., 0., 0., 0.]]),\n",
- " 'b': array([-3.]), 'name': 'custom'}],\n",
- " fit_intercept=False, max_iter=10000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(constraint=[{'A': array([[1., 1., 0., 0., 0.]]),\n",
- " 'b': array([-3.]), 'name': 'custom'}],\n",
- " fit_intercept=False, max_iter=10000)plq_Ridge_Classifier(loss={'name': 'svm'}, max_iter=50000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'svm'}, max_iter=50000)plq_Ridge_Classifier(constraint=[{'name': 'fair', 'sen_idx': [0],\n",
- " 'tol_sen': 0.1}],\n",
- " loss={'name': 'svm'}, max_iter=50000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(constraint=[{'name': 'fair', 'sen_idx': [0],\n",
- " 'tol_sen': 0.1}],\n",
- " loss={'name': 'svm'}, max_iter=50000)plq_Ridge_Regressor(loss={'name': 'mae'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(loss={'name': 'mae'})Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg', plq_Ridge_Regressor(loss={'name': 'mae'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg', plq_Ridge_Regressor(loss={'name': 'mae'}))])StandardScaler()
plq_Ridge_Regressor(loss={'name': 'mae'})plq_Ridge_Regressor(loss={'name': 'mse'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(loss={'name': 'mse'})Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf', plq_Ridge_Regressor(loss={'name': 'mse'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf', plq_Ridge_Regressor(loss={'name': 'mse'}))])StandardScaler()
plq_Ridge_Regressor(loss={'name': 'mse'})plq_Ridge_Regressor(C=0.001, loss={'name': 'QR', 'qt': 0.95})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.001, loss={'name': 'QR', 'qt': 0.95})Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg',\n",
- " plq_Ridge_Regressor(C=0.001,\n",
- " loss={'name': 'QR', 'qt': 0.95}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg',\n",
- " plq_Ridge_Regressor(C=0.001,\n",
- " loss={'name': 'QR', 'qt': 0.95}))])StandardScaler()
plq_Ridge_Regressor(C=0.001, loss={'name': 'QR', 'qt': 0.95})plq_Ridge_Regressor(C=0.005,\n",
- " loss={'epsilon': 0.1, 'name': 'check_eps', 'qt': 0.9})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.005,\n",
- " loss={'epsilon': 0.1, 'name': 'check_eps', 'qt': 0.9})plq_Ridge_Regressor(C=0.005,\n",
- " loss={'epsilon': 0.2, 'name': 'check_eps', 'qt': 0.95})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.005,\n",
- " loss={'epsilon': 0.2, 'name': 'check_eps', 'qt': 0.95})plq_Ridge_Classifier(loss={'name': 'svm'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'svm'})Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf', plq_Ridge_Classifier(loss={'name': 'svm'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf', plq_Ridge_Classifier(loss={'name': 'svm'}))])StandardScaler()
plq_Ridge_Classifier(loss={'name': 'svm'})plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'})Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg',\n",
- " plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg',\n",
- " plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'}))])StandardScaler()
plq_Ridge_Regressor(loss={'epsilon': 0.1, 'name': 'svr'})plq_Ridge_Classifier(loss={'name': 'sSVM'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'sSVM'})Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf', plq_Ridge_Classifier(loss={'name': 'sSVM'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf', plq_Ridge_Classifier(loss={'name': 'sSVM'}))])StandardScaler()
plq_Ridge_Classifier(loss={'name': 'sSVM'})plq_Ridge_Classifier(loss={'name': 'squared SVM'})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Classifier(loss={'name': 'squared SVM'})Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf', plq_Ridge_Classifier(loss={'name': 'squared SVM'}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('clf', plq_Ridge_Classifier(loss={'name': 'squared SVM'}))])StandardScaler()
plq_Ridge_Classifier(loss={'name': 'squared SVM'})plq_Ridge_Regressor(C=0.001, loss={'name': 'huber', 'tau': 5.0})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. plq_Ridge_Regressor(C=0.001, loss={'name': 'huber', 'tau': 5.0})Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg',\n",
- " plq_Ridge_Regressor(C=0.001,\n",
- " loss={'name': 'huber', 'tau': 5.0}))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', StandardScaler()),\n",
- " ('reg',\n",
- " plq_Ridge_Regressor(C=0.001,\n",
- " loss={'name': 'huber', 'tau': 5.0}))])StandardScaler()
plq_Ridge_Regressor(C=0.001, loss={'name': 'huber', 'tau': 5.0})