![](\"https://jupyter.org/assets/share.png\")
주피터 노트북 뷰어로 보기\n",
" ![](\"https://www.tensorflow.org/images/colab_logo_32px.png\"){kind=logo}
구글 코랩(Colab)에서 실행하기\n",
" | \n",
" 주피터 노트북 뷰어로 보기\n",
" | \n",
" \n",
" 구글 코랩(Colab)에서 실행하기\n",
" | \n",
"
Pipeline(steps=[('scaler', MinMaxScaler()), ('svm', SVC())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', MinMaxScaler()), ('svm', SVC())])MinMaxScaler()
SVC()
Pipeline(steps=[('select',\n",
" SelectPercentile(percentile=5,\n",
" score_func=<function f_regression at 0x7c5d2a0d3130>)),\n",
" ('ridge', Ridge())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('select',\n",
" SelectPercentile(percentile=5,\n",
" score_func=<function f_regression at 0x7c5d2a0d3130>)),\n",
" ('ridge', Ridge())])SelectPercentile(percentile=5,\n",
" score_func=<function f_regression at 0x7c5d2a0d3130>)Ridge()
GridSearchCV(cv=5,\n",
" estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
" ('logisticregression',\n",
" LogisticRegression(max_iter=1000))]),\n",
" param_grid={'logisticregression__C': [0.01, 0.1, 1, 10, 100]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5,\n",
" estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
" ('logisticregression',\n",
" LogisticRegression(max_iter=1000))]),\n",
" param_grid={'logisticregression__C': [0.01, 0.1, 1, 10, 100]})Pipeline(steps=[('standardscaler', StandardScaler()),\n",
" ('logisticregression', LogisticRegression(max_iter=1000))])StandardScaler()
LogisticRegression(max_iter=1000)
GridSearchCV(cv=5,\n",
" estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
" ('polynomialfeatures',\n",
" PolynomialFeatures()),\n",
" ('ridge', Ridge())]),\n",
" n_jobs=-1,\n",
" param_grid={'polynomialfeatures__degree': [1, 2, 3],\n",
" 'ridge__alpha': [0.001, 0.01, 0.1, 1, 10, 100]})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=5,\n",
" estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
" ('polynomialfeatures',\n",
" PolynomialFeatures()),\n",
" ('ridge', Ridge())]),\n",
" n_jobs=-1,\n",
" param_grid={'polynomialfeatures__degree': [1, 2, 3],\n",
" 'ridge__alpha': [0.001, 0.01, 0.1, 1, 10, 100]})Pipeline(steps=[('standardscaler', StandardScaler()),\n",
" ('polynomialfeatures', PolynomialFeatures()),\n",
" ('ridge', Ridge())])StandardScaler()
PolynomialFeatures()
Ridge()