Overview of machine learning models

Predicting CTR with Machine Learning in Python

Kevin Huo

Instructor

Logistic regression

Example of Logistic Regression with red and blue dots

Logistic regression: linear classifier between dependent variable and independent variables

Training the model

Can create the model via: clf = LogisticRegression()
Each classifier has a fit() method which takes in an X_train, y_train: clf.fit(X_train, y_train)
X_train is the vector of training features, y_train is the vector of training targets
Classifier should only see training data to avoid "seeing answers beforehand"

Testing the model

Each classifier has a predict() method which takes in an X_test to generate a y_test as follows:
```
array([0, 1, 1, ..., 1, 0, 1])
```

predict_proba() method produces probability scores

array([0.2, 0.8], [0.4, 0.6] ..., [0.1, 0.9] [0.3, 0.7]])

Score reflects probability of a particular ad being clicked by particular user

Evaluating the model

Accuracy: the percentage of test targets correctly identified
accuracy_score(y_test, y_pred)
Should not be the only metric to evaluate model, particularly in imbalanced datasets
CTR prediction is an example where classes are imbalanced

Let's practice!

Predicting CTR with Machine Learning in Python

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