連続した数値を推定 Watson Studioの AutoAI機能で プログラミングなしで モデル作成が可能! AdaBoost Regression Elastic Net with Cross- Validation Nearest Neighbor Analysis (KNN) Lasso Lars with Cross- Validation MLP Regression Orthogonal Matching Pursuit with Cross- Validation Random Forest Regression Theil-Sen Regression ARD Regression Elastic Net Kernel Ridge Lasso Lars MultiTask Elastic Net CV Orthogonal Matching Pursuit RANSAC Regression XGBoost Regression Bayesian Ridge Regression Gaussian Process Lars with Cross- Validation Lasso Lars IC MultiTask Elastic Net Passive- Aggressive Regression Ridge with Cross- Validation CCA Gaussian Process Regression Lars LGBM Regression Multi Task Lasso CV PLS Canonical Ridge Decision Tree Regression Gradient Boosting Regression Lasso with Cross- Validation Linear Regression Multi Task Lasso PLS Regression SGD Regression Extra Trees Regression Huber Regression Lasso Linear Support Vector Regression Nu SVR Radius Neighbors Regression Support Vector Regression AdaBoost Classifier Gaussian Naïve Bayes Classifier Label Spreading Logistic Regression Passive Aggressive Classifier Ridge Classifier with Cross- Validation Bernoulli Naïve Bayes Classifier Gaussian Process Classifier LGBM Classifier MLP Classifier Perceptron Ridge Classifier Calibrated Classifier with Cross-Validation Gradient Boosted Tree Classifier Linear Discriminant Analysis Multinomial Naïve Bayes Classifier Quadratic Discriminant Analysis SGD Classifier Decision Tree Classifier Nearest Neighbor Analysis (KNN) Classifier Linear Support Vector Classifier Nearest Centroid Radius Neighbors Classifier Support Vector Classifier Extra Trees Classifier Label Propagation Logistic Regression with Cross- Validation Nu Support Vector Classifier Random Forest Classifier XGBoost Classifier 分類クラスを推定
Nearest Neighbor Analysis (KNN) Lasso Lars with Cross- Validation MLP Regression Orthogonal Matching Pursuit with Cross- Validation Random Forest Regression Theil-Sen Regression ARD Regression Elastic Net Kernel Ridge Lasso Lars MultiTask Elastic Net CV Orthogonal Matching Pursuit RANSAC Regression XGBoost Regression Bayesian Ridge Regression Gaussian Process Lars with Cross- Validation Lasso Lars IC MultiTask Elastic Net Passive- Aggressive Regression Ridge with Cross- Validation CCA Gaussian Process Regression Lars LGBM Regression Multi Task Lasso CV PLS Canonical Ridge Decision Tree Regression Gradient Boosting Regression Lasso with Cross- Validation Linear Regression Multi Task Lasso PLS Regression SGD Regression Extra Trees Regression Huber Regression Lasso Linear Support Vector Regression Nu SVR Radius Neighbors Regression Support Vector Regression
Nearest Neighbor Analysis (KNN) Lasso Lars with Cross- Validation MLP Regression Orthogonal Matching Pursuit with Cross- Validation Random Forest Regression Theil-Sen Regression ARD Regression Elastic Net Kernel Ridge Lasso Lars MultiTask Elastic Net CV Orthogonal Matching Pursuit RANSAC Regression XGBoost Regression Bayesian Ridge Regression Gaussian Process Lars with Cross- Validation Lasso Lars IC MultiTask Elastic Net Passive- Aggressive Regression Ridge with Cross- Validation CCA Gaussian Process Regression Lars LGBM Regression Multi Task Lasso CV PLS Canonical Ridge Decision Tree Regression Gradient Boosting Regression Lasso with Cross- Validation Linear Regression Multi Task Lasso PLS Regression SGD Regression Extra Trees Regression Huber Regression Lasso Linear Support Vector Regression Nu SVR Radius Neighbors Regression Support Vector Regression
[Existing]のタブが選択された画⾯が表⽰されたにもかかわらず、「Existing Service Instance」の下に「 No existing service instances found 」が表⽰された 場合は、「CLOUD FOUNDRY ORG」のドロップダウンを開き、値を選択してみて ください。。
of us https://www.ibm.com/cloud/garage/dte/tutorial/ibm-watson-studio- autoai-modeling-rest-us のCreate an AutoAI model for regression 部分 (それより前のセクションは当講義の内容です) ⽇本語解説へのLink: http://ibm.biz/WatsonStudioTutrialJP オプション課題: 上記チュートリアルの Accessing a model through a notebook 部分