Binary classification is highly used in credit scoring in the estimation of probability of default. The validation of such predictive models is based both on rank ability, and also on calibration (i.e. how accurately the probabilities output by the model map to the observed probabilities). In this study we cover the current best practices regarding calibration for binary classification, and explore how different approaches yield different results on real world credit scoring data. The limitations of evaluating credit scoring models using only rank ability metrics are explored. A benchmark is run on 18 real world datasets, and results compared. The calibration techniques used are Platt Scaling and Isotonic Regression. Also, different machine learning models are used: Logistic Regression, Random Forest Classifiers, and Gradient Boosting Classifiers. Results show that when the dataset is treated as a time series, the use of re-calibration with Isotonic Regression is able to improve the long term calibration better than the alternative methods. Using re-calibration, the non-parametric models are able to outperform the Logistic Regression on Brier Score Loss.
A logistic regression model is said to be statistically significant only when the p-Values are less than the pre-determined statistical significance level, which is ideally 0.05. The p-value for each coefficient is represented as a probability Pr( z). We see here that both the coefficients have a very low p-value which means that both the coefficients are essential in computing the response variable. The stars corresponding to the p-values indicate the significance of that respective variable. Since in our model, both the p values have a 3 star, this indicates that both the variables are extremely significant in predicting the response variable.
Logistic Regression is one of the most used Machine Learning algorithms for binary classification. It is a simple Algorithm that you can use as a performance baseline, it is easy to implement and it will do well enough in many tasks. Therefore every Machine Learning engineer should be familiar with its concepts. The building block concepts of Logistic Regression can also be helpful in deep learning while building neural networks. In this post, you will learn what Logistic Regression is, how it works, what are advantages and disadvantages and much more.
In supervised learning, algorithms learn from labeled data. After understanding the data, the algorithm determines which label should be given to new data by associating patterns to the unlabeled new data. Supervised learning can be divided into two categories: classification and regression. Some examples of classification include spam detection, churn prediction, sentiment analysis, dog breed detection and so on. Some examples of regression include house price prediction, stock price prediction, height-weight prediction and so on.