FAQ on Machine Learning

For each bootstrap sample, there is one-third of data that was not used in the creation of the tree, i.e., it was out of the sample. This data is referred to as out of bag data. In order to get an unbiased measure of the accuracy of the model over test data, out of bag … Read more

Overfitting is a statistical model or machine learning algorithm which captures the noise of the data. Underfitting is a model or machine learning algorithm which does not fit the data well enough and occurs if the model or algorithm shows low variance but high bias. In decision trees, overfitting occurs when the tree is designed … Read more

Ensemble is a group of models that are used together for prediction both in classification and regression class. Ensemble learning helps improve ML results because it combines several models. By doing so, it allows a better predictive performance compared to a single model. They are superior to individual models as they reduce variance, average out … Read more

Kernel Trick is a mathematical function which when applied on data points, can find the region of classification between two different classes. Based on the choice of function, be it linear or radial, which purely depends upon the distribution of data, one can build a classifier. The Kernel Trick is a fundamental concept in Support … Read more

The function of kernel is to take data as input and transform it into the required form. A few popular Kernels used in SVM are as follows: RBF, Linear, Sigmoid, Polynomial, Hyperbolic, Laplace, etc. In Support Vector Machines (SVM), kernels are a crucial component that allows the algorithm to operate efficiently and effectively in non-linearly … Read more