ML with Python : Zero to Hero | Video 7 | Part 1 | Model Selection | Venkat Reddy AI Classes

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In this video, we delve into the essential aspects of model validation and the metrics used for evaluating regression problems. This comprehensive guide will help you understand how to validate your models and interpret key validation metrics to ensure the accuracy and reliability of your predictions.

Topics Covered:

How to Validate a Model:
Importance of model validation
Techniques for validating models

Validation Metrics for Regression Problems:
Explanation of common metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared

Model Accuracy:
Understanding accuracy in the context of regression
How to measure and interpret accuracy

Sensitivity and Specificity:
Definitions and importance in model evaluation
Practical examples of calculating sensitivity and specificity

Recall and Precision:
Definitions and their roles in evaluating model performance
How to calculate and interpret recall and precision

Types of Errors:
Different types of errors in regression models (e.g., Type I and Type II errors)
Impact of these errors on model performance

By the end of this video, you will have a clear understanding of how to validate your regression models and the metrics used to evaluate their performance, enabling you to build more accurate and reliable models.

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[zixghvv]

Sir really appreciate your way of teaching and the knowledge u share. It's valuable and I learn a lot through your Video.
Sir u mentioned that in case of class Imbalance We can't rely on Accuracy score that is true. But I want to know what if I apply any balancing technique like Smote or any other then can we rely on Accuracy score or we need to focus on Class wise accuracy?

[sourabhjagdale]

oversampling is done before model building or after model building to improve performance?