How can you improve the accuracy of your regression predictions?

To improve the accuracy of your regression predictions, start with DATA QUALITY by cleaning and preprocessing to remove outliers and handle missing values. Use FEATURE ENGINEERING to create relevant variables and enhance model input. Employ FEATURE SELECTION techniques to retain only the most impactful predictors. Choose the right MODEL, comparing different algorithms and tuning hyperparameters for optimal performance. Regularly perform CROSS-VALIDATION to ensure robustness and prevent overfitting. Finally, CONTINUOUSLY UPDATE the model with new data to adapt to changes and maintain accuracy over time.

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Outliers are data points which are significantly different from other observations. We can find them by first finding the IQR and then define outliers as data points outside the range. We also need to compare both datasets to validate data integrity. - Check Bias - Cross Validation

Start with DATA QUALITY, cleaning and preprocessing to eliminate outliers and handle missing values to increase the accuracy of your regression predictions. To improve model input and generate pertinent variables, use FEATURE ENGINEERING. Use FEATURE SELECTION strategies to keep only the most significant predictors. Select the appropriate MODEL by evaluating several algorithms and fine-tuning hyperparameters to get maximum efficiency. Cross-validation should be done frequently to guarantee robustness and avoid overfitting. Lastly, to retain accuracy over time and adjust to changes, the model should be CONTINUOUSLY UPDATED with fresh data.

Yes i totally agree with it and there is one more thing I want to add which is Heatmap visualisation tool. Using this tool we can find best feature. It will show you the correlation between different features and target variable. By looking at the graph you can identify which feature have the strongest positive or negative correlation with target variable.

Improving the accuracy of your regression predictions involves more than just selecting the right features. It requires a combination of practical experience in the industry and a strong academic foundation. Industry experience helps identify subtle, real-world variables and nuances that may not be immediately apparent through data alone. An academic perspective ensures a rigorous, methodical approach to model building. Additionally, carefully choosing control variables is essential to isolate the effects of primary predictors, reducing bias and ensuring the model accurately reflects the relationships in the data. Combining these elements leads to more robust and precise regression models.

Introduce new features that capture underlying patterns or domain-specific knowledge. This increases the model’s capacity to learn complex relationships. Include interaction terms to capture the combined effect of multiple features. L1 Regularization adds a penalty term proportional to the absolute value of the coefficients. It can drive some coefficients to zero, effectively performing feature selection. L2 Regularization adds a penalty term proportional to the square of the coefficients. It reduces the model complexity by shrinking the coefficients, helping to prevent overfitting. By applying regularization techniques, employing ensemble methods, reducing dimensionality, tuning hyperparameters, the predictive power can be enhanced.

In simple terms we don’t want our model to be too much “COMPLEX” ( will fit the training data very well but not do well on new,unseen data) or too “SIMPLE” (will not capture all the details in your data) To avoid overfitting we use these techniques: - Lasso (L1) - Ridge (L2) Cross-validation: Split data into two chunks usually 70-80% for training and 20-30% for testing.

Hyperparameters are high-level settings that influence how the algorithm learns from the data Different hyperparameter configurations can significantly impact the model's ability to capture the underlying relationships within the data. Inappropriate settings can lead to under-fitting (Failing to learn the patterns) or over-fitting (Memorizing the noise in the data) Techniques like Grid Search and Random Forest are really helpful: Grid search systematically evaluates a predefined grid of hyperparameter combinations, while random search samples different configurations at random. Both approaches help identify the hyperparameter settings that yield the best prediction accuracy on a validation set (data not used for training).

Algorithm Tuning: Adjusting the settings of regression algorithm can make it more accurate. Hyper-parameters: With this setting we can control how the model can learn. Example: In ridge regression, you adjust the “alpha” parameter to control the regularization strength. Optimisation Techniques: Grid Search: You systematically test all possible combinations of hyperparameters to find the best one. Random Search: You randomly try different hyperparameter values from a specified range to find a good set.

Data transformation is very effective if the data is very diverse, whit the help of this technique we can normalise all the data. So the model can be better and efficiently trained. There are two methods for simple data transformation: 1. Normalization 2. Standardization If the data is complex, skewed or non-linear we use these methods: 1. Logarithmic 2. Polynomial

Now after creating a regression model, we some time apply ensemble method to approve accuracy of our predictions. In this method we take two or more model and perform ensemble method on that. Two ensembles are used to train the models. 1. Boosting: Weak model to strong, build model sequentially and each new model correcting error made by previous ones. 2. Bagging: Reduce variance by averaging predictions from different models,It builds models independently and then averaging the predictions of these models. Overall this is great way to reduce the biasses and improve accuracy.

The goal of regression is to make relationships between input features and predictive outcome, and using these outcomes make predictions on new/unseen data. Classification usually used for discrete data while regression for continuous (weight, height, house pricing, temperature) Linear and logistics regression are popular types of regression. Model Evaluation: Accuracy of these model usually evaluated through the following methods ? Mean Squared Error ? Root mean squared Error ? Mean Absolute Error ? R-Squared (Coefficient of determinant)