Machine learning training and hyperparameter optimization
We are all set to do the fun part, training ML models! This step enables model training; it has modular scripts or code that perform all the traditional steps in ML training, such as fitting and transforming data to train the model and hyperparameter tuning to converge the best model. The output of this step is a trained ML model.
To solve the business problem, we will train two well-known models using the Support Vector Machine classifier and the Random Forest classifier. These are chosen based on their popularity and consistency of results; you are free to choose models of your choice – there are no limitations in this step. First, we will train the Support Vector Machine classifier and then the Random Forest classifier.
Support Vector Machine
Support Vector Machine (SVM) is a popular supervised learning algorithm (used for classification and regression). The data points are classified using hyperplanes in...
