Scikit Learn

Logistic Regression Is used for classification problems, outputs probabilities if > 0.5 the data is labeled 1 else it is labeled 0. Produces a linear decision boundary. from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split logreg = LogisticRegression() X_train, X_text, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42) logreg.fit(X_train,y_train) y_pred= logreg.predict(X_test) ROC Curve Stands for Receiver Operating Characteristics curve…

Fast.AI

  Fast.AI Update Feb-2019 Deep learning V3 Kernels Lesson 1 Pets Lesson 2 Download Lesson 2 SGD Lesson 3 Camvid-tiramisu Lesson 3 Camvid Lesson 3 Head-Pose Lesson 3 Planet Lesson 3 Tabular Lesson 4 Collab Lesson 4 Tabular Lesson 5 SGD-MNIST Lesson 6 Pets-more Rossmann data clean Lesson 6 Rossmann Lesson 7 Human-numbers [Lesson 7…

100DaysOfMLCode Index

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