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#### Overview of this book

More than 80,000 Kaggle novices currently participate in Kaggle competitions. To help them navigate the often-overwhelming world of Kaggle, two Grandmasters put their heads together to write The Kaggle Book, which made plenty of waves in the community. Now, they’ve come back with an even more practical approach based on hands-on exercises that can help you start thinking like an experienced data scientist. In this book, you’ll get up close and personal with four extensive case studies based on past Kaggle competitions. You’ll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. Get into computer vision by discovering different solutions for identifying the type of disease present on cassava leaves. And see how the Kaggle community created predictive algorithms to solve the natural language processing problem of subjective question-answering. You can use this workbook as a supplement alongside The Kaggle Book or on its own alongside resources available on the Kaggle website and other online communities. Whatever path you choose, this workbook will help make you a formidable Kaggle competitor.
Preface
Free Chapter
The Most Renowned Tabular Competition – Porto Seguro’s Safe Driver Prediction
The Makridakis Competitions – M5 on Kaggle for Accuracy and Uncertainty
Vision Competition – Cassava Leaf Disease Competition
NLP Competition – Google Quest Q&A Labeling
Other Books You May Enjoy
Index

# Understanding the Evaluation Metric

The accuracy competition introduced a new evaluation metric: Weighted Root Mean Squared Scaled Error (WRMSSE). You first start from the RMSSE of individual time series under scrutiny. The metric evaluates the deviation of the point forecasts around the mean of the realized values of the series being predicted:

where:

• n is the length of the training sample
• h is the forecasting horizon (in our case, it is h =28)
• Yt is the sales value at time t; is the predicted value at time t

After estimating the RMSSE for all the 42,840 time series of the competition, the Weighted RMSSE will be computed as:

where wi is the weight of the ith series of the competition.

In the competition guidelines (https://mofc.unic.ac.cy/m5-competition/), in regard to RMSSE and WRMSSE, it is stated that:

• The denominator of RMSSE is computed only for the time periods for which the examined product(s) are actively sold...