September 29th
K- cross Validation
K-fold cross-validation is a technique commonly used in machine learning and statistical modeling to assess the performance and generalization ability of a predictive model. It is particularly helpful when you have a limited amount of data and want to make efficient use of it while avoiding overfitting. K-fold cross-validation involves the following steps:
- Data Splitting
- Model Training and Evaluation
- Performance Metric Calculation
- Cross-Validation Results
October 2nd
We used Regularization concept in our project. Regularization is a set of techniques used to prevent overfitting and improve the generalization performance of statistical models. Overfitting occurs when a model learns to fit the training data too closely, capturing noise and idiosyncrasies in the data rather than the underlying patterns. This can lead to poor performance when the model is applied to new, unseen data. Regularization methods are particularly important in situations where the number of features (variables) is large relative to the number of observations (data points), which is common in many statistical modeling problems. Regularization helps to control the complexity of a model and reduce the risk of overfitting.