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Statistics for Machine Learning

You're reading from   Statistics for Machine Learning Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781788295758
Length 442 pages
Edition 1st Edition
Languages
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Author (1):
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Pratap Dangeti Pratap Dangeti
Author Profile Icon Pratap Dangeti
Pratap Dangeti
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Table of Contents (10) Chapters Close

Preface 1. Journey from Statistics to Machine Learning 2. Parallelism of Statistics and Machine Learning FREE CHAPTER 3. Logistic Regression Versus Random Forest 4. Tree-Based Machine Learning Models 5. K-Nearest Neighbors and Naive Bayes 6. Support Vector Machines and Neural Networks 7. Recommendation Engines 8. Unsupervised Learning 9. Reinforcement Learning

Variable importance plot

Variable importance plot provides a list of the most significant variables in descending order by a mean decrease in Gini. The top variables contribute more to the model than the bottom ones and also have high predictive power in classifying default and non-default customers.

Surprisingly, grid search does not have variable importance functionality in Python scikit-learn, hence we are using the best parameters from grid search and plotting the variable importance graph with simple random forest scikit-learn function. Whereas, in R programming, we have that provision, hence R code would be compact here:

>>> import matplotlib.pyplot as plt 
>>> rf_fit = RandomForestClassifier(n_estimators=1000, criterion="gini", max_depth=300, min_samples_split=3,min_samples_leaf=1) 
>>> rf_fit.fit(x_train,y_train)    
>>>...
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