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Book Overview & Buying
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Table Of Contents
The Kaggle Book - Second Edition
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How a Kaggle solution performs is not simply determined by the type of learning algorithm you choose. Aside from the data and features you use, it is also strongly determined by the algorithm’s hyperparameters, which must be fixed before training and cannot be learned during training. Choosing suitable features is most effective in tabular data competitions; however, hyperparameter optimization is effective in all competitions of any kind. In fact, given fixed data and an algorithm, hyperparameter optimization is the only sure way to enhance the predictive performance of the algorithm and climb the leaderboard. It also helps in ensembling because an ensemble of tuned models always outperforms an ensemble of untuned ones.
This chapter is certainly one of the most challenging in the book, but mastering the techniques discussed here will yield significant rewards. It is addressed to readers with an intermediate level of competence in machine...