Model Training and Evaluation are crucial steps in Machine Learning that ensure a model learns correctly from data and performs well on unseen data.
Model training is the process where a machine learning algorithm learns patterns from training data by adjusting its parameters.
Steps in Model Training:
Select a suitable algorithm
Provide training data
Optimize model parameters
Minimize errors using a loss function
Goal:
To build a model that can make accurate predictions.
Model evaluation measures how well the trained model performs on new or unseen data.
Why Evaluation is Important:
Prevents overfitting
Checks real-world performance
Helps compare different models
For Classification Models:
Accuracy
Precision
Recall
F1-Score
Confusion Matrix
For Regression Models:
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
R² Score
Data is divided into training data and testing data
Common split: 80% training – 20% testing
Data is split into multiple parts (folds)
Model is trained and tested multiple times
Improves reliability of results
Training helps the model learn from data
Evaluation ensures the model performs accurately and reliably
Both steps are essential for building a successful machine learning system
Take quizzes related to this topic and see where you stand!
Start Quiz Now