Category «Machine Learning»

Time Series Analysis

Time Series is a statistical data that are arranged in chronological order over a period of time. There are various forces that affect the value of phenomenon in a time series, these may be divided into four categories, commonly known as the factor of time series. 1- Simple trend or Long-term variation or Secular trend. …

Machine is learning Hindi

How much time we have spent in order to learn ‘ABCD..’ it’s about a year. Now, we are living in the era of machines and assume your machine asks you to teach ‘Hindi’ –the fourth most speaking language around the world. With the help of deep learning, it is too easy and effective. So moving …

Imbalanced-Data Set for Classification

Machine Learning algorithms unsatisfied problem with classifiers when faced with imbalanced datasets. Let’s take an example of the Red-wine problem. The Data we have is as: Here we have a data set in which we have different six categories, but not balanced categories. So, in the case available the samples are not equally balanced so …

Machine learning classification

Introduction Supervised machine learning has two types: 1) Regression,  2) Classification In regression, we deal with the continuous data type that means the predicted value or target value will be the type of continuous in nature but in classification problems, we basically deal with target value as discrete in nature it belongs to some class. Let’s understand some use …

Decision Tree

Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. It works for both categorical and continuous input and output variables. It is a non-parametric algorithm. It uses following algorithms. 1- ID3: ID3 uses Entropy and Information gain to construct a decision tree. 2- Gini Index (, if we select two items from a population at …

KNN

KNN is another supervised classification technique, which makes no assumption of distribution of data. It is a non-parametric algorithm.It is the simplest technique of classification, the algorithm used for this technique is as: Algorithm Let m be the number of training data samples. Let p be an unknown point. Store the training samples in an …