Advanced Scikit Learn Andreas Mueller (NYU Center for Data Science, scikit learn) 2 Me 3 Classification Regression Clustering Semi Supervised Learning Feature Selection Feature Extraction Manifold L.
Advanced Scikit-Learn Andreas Mueller (NYU Center for Data Science, scikit-learn) Me Classification Regression Clustering Semi-Supervised Learning Feature Selection Feature Extraction Manifold Learning Dimensionality Reduction Kernel Approximation Hyperparameter Optimization Evaluation Metrics Out-of-core learning … Overview ● Reminder: Basic sklearn concepts ● Model building and evaluation: ● ● – Pipelines and Feature Unions – Randomized Parameter Search – Scoring Interface Out of Core learning – Feature Hashing – Kernel Approximation New stuff in 0.16.0 – Overview – Calibration Supervised Machine Learning clf = RandomForestClassifier() Training Data clf.fit(X_train, y_train) Model Training Labels Supervised Machine Learning clf = RandomForestClassifier() Training Data clf.fit(X_train, y_train) Model Training Labels y_pred = clf.predict(X_test) Test Data Prediction Supervised Machine Learning clf = RandomForestClassifier() Training Data clf.fit(X_train, y_train) Model Training Labels y_pred = clf.predict(X_test) clf.score(X_test, y_test) Test Data Prediction Test Labels Evaluation Unsupervised Transformations pca = PCA(n_components=3) pca.fit(X_train) Training Data Model Unsupervised Transformations pca = PCA(n_components=3) pca.fit(X_train) X_new = pca.transform(X_test) Training Data Model Test Data Transformation 10 (RBF) Kernel SVM 84 (RBF) Kernel SVM 85 (RBF) Kernel SVM 86 Decision Trees 87 Decision Trees 88 Decision Trees 89 Decision Trees 90 Decision Trees 91 Decision Trees 92 Random Forests 93 Random Forests 94 Random Forests 95 Know where you are on the bias-variance tradeoff 96 Validation Curves train_scores, test_scores = validation_curve(SVC(), X, y, param_name="gamma", param_range=param_range) 97 Learning Curves train_sizes, train_scores, test_scores = learning_curve( estimator, X, y,train_sizes=train_sizes) 98 ... Semi-Supervised Learning Feature Selection Feature Extraction Manifold Learning Dimensionality Reduction Kernel Approximation Hyperparameter Optimization Evaluation Metrics Out-of-core learning …... Overview ● Reminder: Basic sklearn concepts ● Model building and evaluation: ● ● – Pipelines and Feature Unions – Randomized Parameter Search – Scoring Interface Out of Core learning – Feature Hashing... Overview – Calibration Supervised Machine Learning clf = RandomForestClassifier() Training Data clf.fit(X_train, y_train) Model Training Labels Supervised Machine Learning clf = RandomForestClassifier()