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rohitinu6 / Stock-Price-Prediction

This project focuses on predicting the stock prices of "The State Bank Of India" using machine learning Regression algorithms.
MIT License
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Removal of Classification Metrics from Regression Model Evaluation #35

Open AnujSaha0111 opened 1 month ago

AnujSaha0111 commented 1 month ago

I’ve also noticed that the evaluation of the regression model includes classification metrics such as accuracy, precision, recall, F1 score, and confusion matrix. These metrics are specifically designed for classification tasks where the goal is to predict discrete labels. However, in project which involves Stock Price Prediction which is a regression tasks, the objective is to predict continuous values, making these metrics irrelevant and potentially misleading when evaluating model performance.

Since metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are already implemented and are appropriate for this regression task, I suggest removing the classification metrics. This will help maintain clarity and focus on relevant performance indicators.

AnujSaha0111 commented 1 month ago

Can you please assign me the issue?

AnujSaha0111 commented 1 month ago

Issue is solved in https://github.com/rohitinu6/Stock-Price-Prediction/pull/37

rohitinu6 commented 1 month ago

@AnujSaha0111 Thank you for your valuable contribution, Please mention this issue number in the description of your PR. Only those PR, that solve an existing issue will be considered for evaluation by the mentors.

AnujSaha0111 commented 1 month ago

Done👍

AnujSaha0111 commented 1 month ago

Was it merged?