Logarithmic loss (or cross-entropy) measures the performance of a classification model where the prediction input is a probability value between 0 and 1. The goal of our machine learning models is to minimize this value. It is also heavily used in Kaggle competitions to estimate the score of submissions. A Read more
For binary classification problems, a good way to measure the performance of a model is by finding out AUC (Area Under The Curve) of ROC (Receiver Operating Characteristics). What is a ROC curve? It is a plot of True Positive Rate(TPR) vs FPR at various thresholding levels. Let’s understand this Read more
Confusion Matrix Confusion Matrix is a group or matrix of metrics in supervised learning scenarios which determine how good a model is in predictions. Lets consider a binary classifier with results 1 or 0. A confusion matrix here will be a 2×2 matrix where along one axis you have actual Read more
In statistics, a confidence interval (CI) is a type of interval estimate which we compute using the statistics of the observed data. The interval has an associated confidence level that, loosely speaking, quantifies the level of confidence that the value of the parameter lies in the interval. For eg, if Read more
Suppose you have taken the data for heights and weights of students in class and you want to figure out the correlation between heights and weights of students. The relation between these two parameters is defined mathematically by one of the 3 ways 1) Covariance 2) Pearson Correlation Coefficient 3) Read more