## Stopping Criteria for Decision tree Algorithm and tree Plots

Decision Tree is an algorithm build for Machine learning purposes which works on the concepts of dividing data into subsets. It means it works to give you subsets representing only one type of category or values within particular range. But there are certain drawback that need to be discussed.

## ML Algorithms – Which one is better ?

In every situation we went through numerous option same as here. Whenever we decided to work on ML, we do stuck into a thought process of selecting an algorithm which suits our need. Its also bit complicated to know all the minor details of each one of them. Then how can we decide X is […]

## Dimensionality Reduction – (PCA)

Variations in the dataset is actually the information from the dataset and this is what the PCA uses. In simple terms PCA or Principal component analysis is a process to emphasise variations in a data set and generate strong pattern out of it. We can figure out the whole concepts in 3 points as follows— […]

## Mean, Variance, Standard Deviation, Standard Score, Covariance & Data Projection

Variance – It is the measure of squared difference from the Mean. To calculate it we follow certain steps mentioned below: Calculate average of numbers For each numbers subtract the mean and square the result Calculate the average of those squared differences i.e. Variance

## Eigenvector represents greatest variance in case of PCA

In case of Principal Component Analysis we project our data points on a vector in a direction of maximum variance to decrease the number of existing components. In this case we consider the direction eigenvector generated using covariance matrix as the direction of maximum variance. In this article we look into the proof of why […]

## Eigen Vector and Eigen Values

Eigen vector is the direction in a coordinate space defined by a metrics which doesn’t change its direction with metrics transformation. Eigen value is a scaler number which is multiplied with Eigen vector to give same result as Eigen vector multiplier with existing metrics.