5 Tricks to Improve Your Machine Learning Models
Improving Machine Learning model can be challenging sometime. Even after trying all the strategies which you have learned, you would not get that accuracy which you are looking for. You feel irritated and helpless and this is where most of the data scientists give up. In order to become a master data scientist, you have to be different from an average data scientist. This article will talk about 5 strategies for restructuring your model approach, with the goal of enhancing its accuracy.
1 – Feature Scaling
One of the most common mistakes in machine learning is not scaling your features properly. Many machine learning algorithms rely on distance calculations, and if the features are not on the same scale, some features will dominate the distance calculations. There are many methods to scale your features, two of them are MinMaxScaler and StandardScaler from scikit-learn.
2 – Feature Selection
Not all features are created equal, and some may be more important than others. Feature selection methods help you identify the most important features for your model, which can help you improve performance and reduce overfitting. There are many methods to perform feature selection, including Recursive Feature Elimination and SelectKBest from scikit-learn.
3 – Ensemble Methods
Ensemble methods combine multiple models to improve performance. Two popular ensemble methods are Random Forests and Gradient Boosting. Random Forests build multiple decision trees and combine their predictions to reduce overfitting. Gradient Boosting builds an ensemble of models in a stage-wise fashion, where each new model tries to correct the mistakes which previous model has done previous.
4 – Hyperparameter Tuning
Most machine learning algorithms have hyperparameters that need to be tuned to achieve optimal performance. Grid search and RandomizedSearchCV from scikit-learn are two popular methods for hyperparameter tuning.
5 – Cross-Validation
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves splitting the data into multiple folds, training the model on each fold, and evaluating the performance on the remaining folds. This helps to ensure that your model is not overfitting to the data. scikit-learn has several methods for performing cross-validation, including KFold and StratifiedKFold.
In summary, improving your machine learning models requires careful attention to feature scaling, feature selection, ensemble methods, hyperparameter tuning, and cross-validation. By implementing these tricks, you can improve the performance of your models and achieve better results.