sirf accuracy ko evaluate karna ho to accuracy score use karein ga

1- Accuracy , 2-Recall , 3-Precision , 4-F1 Score , 5-Confusion Metrics

learn evluation metrics regression and classifition

Metrics of logostic regression(classification) are.....1- Accuracy,2-Recall,3-Precision,4-F1 score,5-confusion metrics

The main metrics used to evaluate logistic regression models are:1. Accuracy:The fraction of correct predictions out of the total number of predictions. It is calculated as (correct predictions) / (total predictions). 2. Precision: The fraction of true positive predictions out of all positive predictions. It measures how precise the model is at predicting the positive class. 3. Recall (Sensitivity): The fraction of true positive predictions out of all actual positive instances. It measures how well the model identifies positive instances. 4. F1-Score: The harmonic mean of precision and recall. It provides a balanced metric that considers both precision and recall. ROC (Receiver Operating Characteristic) Curve: A plot of the true positive rate (recall) against the false positive rate (1 - specificity) at different classification thresholds. The AUC (Area Under the Curve) is a useful metric derived from the ROC curve. 5. Log Loss (Cross-Entropy Loss): The negative log-likelihood of the true labels given the predicted probabilities. It penalizes confident incorrect predictions more heavily.

The main metrics used to evaluate logistic regression models are: Accuracy: The fraction of correct predictions out of the total number of predictions. It is calculated as (correct predictions) / (total predictions). Precision: The fraction of true positive predictions out of all positive predictions. It measures how precise the model is at predicting the positive class. Recall (Sensitivity): The fraction of true positive predictions out of all actual positive instances. It measures how well the model identifies positive instances. F1-Score: The harmonic mean of precision and recall. It provides a balanced metric that considers both precision and recall. ROC (Receiver Operating Characteristic) Curve: A plot of the true positive rate (recall) against the false positive rate (1 - specificity) at different classification thresholds. The AUC (Area Under the Curve) is a useful metric derived from the ROC curve. Log Loss (Cross-Entropy Loss): The negative log-likelihood of the true labels given the predicted probabilities. It penalizes confident incorrect predictions more heavily.

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I learned in this lecture regression and classification matrics.

I learned Regression and Classification.

Regression and Classification Matrices overview discussed in this lecture. (1) Mean Squared Error - MSE (2) R2-Coefficient of Determination (3) Root Mean Squared Error (4) Mean Absolute Error (5) Accuracy Score (6) Recall Score (7) Precision Score (8) F1 Score (9) Confusion Matrix (10) Classification Report.