O'Reilly logo

Test-Driven Machine Learning by Justin Bozonier

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Cross-validating our model

Now before we cheat and look at our answer key, let's see how well this solution does at predicting data it hasn't seen. To do this, I write the following fairly large test:

def final_model_cross_validation_test(): df = pandas.read_csv('./generated_data.csv') df['predicted_dependent_var'] = 25.6266 \ + 2.7083*df['ind_var_a'] \ - 1.5527*df['ind_var_b'] \ - 0.3917*df['ind_var_c'] \ - 0.2006*df['ind_var_e'] \ + 5.6450*df['ind_var_b'] * df['ind_var_c'] df['diff'] = (df['dependent_var'] - df['predicted_dependent_var']).abs() print df['diff'] print '===========' cv_df = pandas.read_csv('./generated_data_cv.csv') cv_df['predicted_dependent_var'] = 25.6266 \ + 2.7083*cv_df['ind_var_a'] \ - 1.5527*cv_df['ind_var_b'] \ - 0.3917*cv_df['ind_var_c'] ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required