Month: June 2021

Categories
Data Mining

Random Forest vs Gradient boosting

The objective of the task is to build a model so that we can, as optimally as this data allows, relate molecular information, to an actual biological response.

We have shared the data in the comma separated values (CSV) format. Each row in this data set represents a molecule. The first column contains experimental data describing an actual biological response; the molecule was seen to elicit this response (1), or not (0). The remaining columns represent molecular descriptors (D1 through D1776), these are calculated properties that can capture some of the characteristics of the molecule – for example size, shape, or elemental constitution. The descriptor matrix has been normalized.

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Categories
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.

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