Random Forest Classifier in SciKitLearn for Binary Classification - Base problem category as per Ready Tensor specifications.

• random forest
• ensemble
• binary classification
• sklearn
• python
• pandas
• numpy
• HyperOpt
• fastapi
• nginx
• uvicorn
• docker

This is a Binary Classifier that uses a Random Forest implementation through SciKitLearn.

The classifier starts by creating an ensemble of decision trees and assigns the sample to the class that is predicted by the majority of the decision trees.

The data preprocessing step includes missing data imputation, standardization, one-hot encoding for categorical variables, datatype casting, etc. The missing categorical values are imputed using the most frequent value if they are rare. Otherwise if the missing value is frequent, they are give a "missing" label instead. Missing numerical values are imputed using the mean and a binary column is added to show a 'missing' indicator for the missing values. Numerical values are also scaled using a Yeo-Johnson transformation in order to get the data close to a Gaussian distribution.

Hyperparameter Tuning (HPT) is conducted using HyperOpt by finding the optimal number of decision trees to use in the forest, number of samples required to split an internal node, and number of samples required to be at a leaf node.

During the model development process, the algorithm was trained and evaluated on a variety of datasets such as email spam detection, customer churn, credit card fraud detection, cancer diagnosis, and titanic passanger survivor prediction.

This Binary Classifier is written using Python as its programming language. Scikitlearn is used to implement the main algorithm, create the data preprocessing pipeline, and evaluate the model. Numpy, pandas, and feature_engine are used for the data preprocessing steps. HyperOpt was used to handle the HPT. fastapi + Nginx + uvicorn are used to provide web service which includes two endpoints- /ping for health check and /infer for predictions in real time.