howeverand this is major problemzero training error does not mean that your solution will generalize well in factit may generalize very poorly whereas before regularization could give you little extra boostit is now completely required for meaningful result an example based on text we will now turn to an example which comes from study performed at carnegie mellon university by prof noah smith' research group the study was based on mining the so-called " - reportsthat companies file with the securities and exchange commission (secin the united states this filing is mandated by the law for all publicly-traded companies the goal is to predictbased on this piece of public informationwhat the future volatility of the company' stock will be in the training datawe are actually using historical data for which we already know what happened there are , examples available the features correspond to different words , in totalwhich have already been preprocessed for us thuswe have many more features than examples the dataset is available in svmlight format from multiple sourcesincluding the book' companion website this is format which scikit-learn can read svmlight isas the name saysa support vector machine implementationwhich is also available through scikit-learnright nowwe are only interested in the file format from sklearn datasets import load_svmlight_file data,target load_svmlight_file(' train'in the preceding codedata is sparse matrix (that ismost of its entries are zeros andthereforeonly the non-zero entries are saved in memory)while target is simple one-dimensional vector we can start by looking at some attributes of targetprint('min target value{}format(target min())print('max target value{}format(target max())print('mean target value{}format(target mean())print('std dev target{}format(target std())this prints out the following valuesmin target value- max target value- mean target value- std dev target