Instantiate pipeline

In order to make your life easier as you start to work with all of the data in your original DataFrame, df, it's time to turn to one of scikit-learn's most useful objects: the Pipeline.

For the next few exercises, you'll reacquaint yourself with pipelines and train a classifier on some synthetic (sample) data of multiple datatypes before using the same techniques on the main dataset.

The sample data is stored in the DataFrame, sample_df, which has three kinds of feature data: numeric, text, and numeric with missing values. It also has a label column with two classes, a and b.

In this exercise, your job is to instantiate a pipeline that trains using the numeric column of the sample data.

Import Pipeline from sklearn.pipeline.
Create training and test sets using the numeric data only. Do this by specifying sample_df[['numeric']] in train_test_split().
Instantiate a pipeline as pl by adding the classifier step. Use a name of 'clf' and the same classifier from Chapter 2: OneVsRestClassifier(LogisticRegression()).
Fit your pipeline to the training data and compute its accuracy to see it in action! Since this is toy data, you'll use the default scoring method for now. In the next chapter, you'll return to log loss scoring.

Exercise

Instantiate pipeline

Instructions