Data Mining

Bagging and Random Forest

In this post we do several tasks performing the Bagging and the Random Forest Classificators.

We gradually develop classifier for the Bagging on randomized trees that in its final stage matches the Random Forest algorithm.

We’ll also build the RandomForestClassifier of sklearn.ensemble and learn of its quality depending on (1) number of trees, (2) max features used for each tree node, and (3) max tree depth.

Bagging is an ensemble algorithm that fits multiple models on different subsets of a training dataset, then combines the predictions from all models.

Random forest is an extension of bagging that also randomly selects subsets of features used in each data sample.

Data Mining

Bike Sharing Demand Problem, part 2 – Sklearn SGD regression model, scaling, transformation chain and Random Forest nonlinear model

The Bike Sharing Demand problem requires using historical data on weather conditions and bicycle rental to predict the number of occupied bicycles (rentals) for a certain hour of a certain day.

In the original problem statement, there are 11 features available. The feature set contains both real, categorical, and binary data. For the demonstration, a training sample bike_sharing_demand.csv is used from the original data.

See the Bike Sharing Demand, part 1 of the task where we performed some initial problem analysis.