Day 82: Regression metrics #dataanalysis #coding #datascience #softwaredeveloper #100daysoflearning

Day 82 : Secrets of Regression Metrics

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**Importance of Regression Metrics:**

Regression metrics are essential tools for evaluating the performance of a regression model. They provide insights into how well the model fits the data and predicts future outcomes.

**5 Types of Regression Metrics:**

1. **Mean Squared Error (MSE):** Measures the average squared difference between the predicted values and the actual values. Lower MSE indicates a better fit.

2. **Mean Absolute Error (MAE):** Measures the average absolute difference between the predicted values and the actual values. It is less sensitive to outliers than MSE.

3. **Root Mean Squared Error (RMSE):** The square root of MSE. It is expressed in the same units as the target variable, making it easier to interpret.

4. **R2 Score:** Measures the proportion of variance in the target variable that is explained by the model. A higher R2 score indicates a better fit.

5. **Adjusted R2 Score:** A modified version of the R2 score that adjusts for the number of predictors in the model. It penalizes models with too many predictors, which can lead to overfitting.

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#DataScience #SimpleLinearRegression #MachineLearning #LearningJourney #CodewithAnkitto

[asdfgadf-wqmk]

bhai ai developer road map pe short bana do

[tihsrah]

Bro should have studied this at 2nd week or so