What is Regression?
54
Building a single variable regressor
55
Building a multivariable regressor
58
Estimating housing prices using a Support Vector Regressor
60
Summary
62
Chapter 3: Predictive Analytics with Ensemble Learning
63
What is Ensemble Learning?
63
Building learning models with Ensemble Learning
64
What are Decision Trees?
64
Building a Decision Tree classifier
65
What are Random Forests and Extremely Random Forests?
70
Building Random Forest and Extremely Random Forest classifiers
70
Estimating the confidence measure of the predictions
76
Dealing with class imbalance
80
Finding optimal training parameters using grid search
87
Computing relative feature importance
90
Predicting traffic using Extremely Random Forest regressor
93
Summary
96
Chapter 4: Detecting Patterns with Unsupervised Learning
97
What is unsupervised learning?
97
Clustering data with K-Means algorithm
98
Estimating the number of clusters with Mean Shift algorithm
104
Estimating the quality of clustering with silhouette scores
107
What are Gaussian Mixture Models?
112
Building a classifier based on Gaussian Mixture Models
113
Finding subgroups in stock market using Affinity Propagation model
118
Segmenting the market based on shopping patterns
120
Summary
124
Chapter 5: Building Recommender Systems
125
Creating a training pipeline
125
Extracting the nearest neighbors
128
Building a K-Nearest Neighbors classifier
132
Computing similarity scores
139
Finding similar users using collaborative filtering
143
Building a movie recommendation system
146
Summary
149
Chapter 6: Logic Programming
150
