sampling – Data and Machine by viswateja

Sampling With/Without Replacement

Sampling is the part of inferential statistics which is use to estimate the population based on the sample data and it is one of the important technique in statistics and Machine learning.In this post we will learn about sampling with replacement and without replacement. Sampling with replacement Let’s take an example we have below listContinue reading “Sampling With/Without Replacement”

Stratified sampling

Imbalanced data is one of the major issue in classification problem. Why we will have imbalanced data? Let’s say if i have 100 customer who is holding credit card, may be maximum I may have 2 or 3% defaulters and remaining 95 to 97% are perfect payers (This is called presence of minority class ),Continue reading “Stratified sampling”

Bootstrap Sample

Before we discuss about Bootstrap Sample, read about Sampling With Replacement and Sampling Without Replacement A bootstrap sample is a random sample that is performed with replacement. Bootstrapping is a resampling with replacement which uses sampling with replacement, It will generate N number of samples and each sample is the same size of population. Let’sContinue reading “Bootstrap Sample”

Random Forest

Before we discuss about Bagging and Random forest we have to understand about Bootstrap sample. Bagging: Is also called bootstrap aggregator it gives best accuracy than decision tree and to reduce the variance. Bagging is very easy when you know how Decision tree and bootstrap sample works.It will use the greedy search algorithms like Entropy, Gini,Continue reading “Random Forest”