Highlighting model selection & hyperparameter tuning, this notebook presents a by-guide on how to effectively utilize machine learning models. By investigating several algorithms and approaches, participants receive practical experience for boosting model performance.
Methodology:
- Examining and understanding the dataset in enormous detail helps to identify significant patterns and
- Evaluating several machine learning techniques—Logistic Regression, Decision Tree, Random Forest, as well as Support Vector Machine
- Grid Search and Random Search belong to the methods used in hyperparameter tuning to identify the most effective hyperparameters for every model.
- Comparatively evaluating several models using criteria including precision, recall, accuracy and F1-score provides
- Visualization: visual methods to explain the effect of several hyperparameters on the model
The outcomes show the carefully selecting the model and optimizing its hyperparameters to generate machine learning models having excellent results. Learners may use this notebook as a great tool for improving their model optimization skills.