Logistic Regression and regularization: Avoiding overfitting and improving generalization

Logistic regression is a widely used classification algorithm that uses a linear model to predict the probability of a binary outcome. It is a simple and effective way to model binary data, but it can sometimes suffer from overfitting and poor generalization to new data. Regularization is a technique that can help mitigate these issues and improve the performance of logistic regression models. <img alt="" src="https://miro.medium.com/v2/resize:fit:700/0*YrumY_k6Tdrd5wfM" style="height:468px; width:700px" /> <h1>Overfitting in logistic regression</h1> <a href="https://medium.com/@rithpansanga/how-to-tell-if-your-machine-learning-model-is-overfitting-and-what-to-do-about-it-2c589ff398a9" rel="noopener">Overfitting</a> occurs when a model is too complex and fits the training data too closely, resulting in poor generalization to new data. This can be a problem for logistic regression models, as they can become overly complex if there are many features or the relationship between the features and the target is non-linear. Overfitting can lead to poor performance on the test set and low predictive power on new data. <a href="https://medium.com/@rithpansanga/logistic-regression-and-regularization-avoiding-overfitting-and-improving-generalization-e9afdcddd09d">Click Here</a>