Random forest algorithm is a supervised learning algorithm. It is an ensemble learning algorithm which means it combines multiple algorithm of the same type and takes the average to improve the predictive accuracy. The random forest can be used for both regression and classification tasks. Ensemble learning In ensemble learning, we join different types of …
Deep learning is a branch of artificial intelligence working by neural networks concept. Deep learning model concept is inspired from our human brain and the neural network model is the replica of the brain. In Deep Learning, ‘deep’ refers to the many number of layers through which the data is transformed. Neural networks can be …
Reinforcement learning (RL) is a type of Machine Learning associated with how intelligent agents should take actions in an environment to maximize rewards. It is employed by finding the best possible path or behavior it should make in a specific situation. The agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves …
The Naive Bayes is a supervised machine learning algorithm for classification. It is fast to build models and make predictions with Naive Bayes algorithm. Text classification is one of the most popular applications of a Naive Bayes classifier. The Naive Bayes algorithm is called naive because the occurrence of a certain feature is independent of …
Decision tree algorithm is a commonly used supervised learning algorithm, solves classification problems. Decision tree is the graphical representation of all the possible solutions to a decision. Decisions are based on some conditions. Decision tree is a tree-like structure, that breaks down a dataset into smaller and smaller subsets. The final result is a tree with decision …
K-Nearest Neighbors(KNN) is one of the simplest supervised machine learning algorithms used for classification. KNN algorithm performs by matching the test data with K neighbor training examples and decides its group. K is the number of neighbors in KNN. The KNN algorithm is used in the following scenarios: Data is noise-free Data is labeled Dataset …
Introduction to Logistic Regression Logistic Regression in Machine Learning is a statistical method for predicting results in discrete values. Logistic Regression is mainly used for classification problems. The output of a Logistic regression model is a probability. It uses the variables in a categorical way, not continuous. i.e., High or low, 0 Or 1, True or …
Simple Linear Regression using scikit-learn Linear Regression is a statistical model used to predict the linear relationship between two or more variables. Here we are going to demonstrate the linear Regression model using the Scikit-learn library in Python. Scikit-learn also defined as sklearn is a python library with a lot of efficient tools for machine learning …
LINEAR REGRESSION: Linear regression is a Supervised Machine Learning algorithm. It is a statistical model used to predict the linear relationship between two or more variables. Mostly we treat datasets with quantitative values as regression models. Linear regression models use a straight line, logistic and nonlinear regression models use a curved line. There are two …
DATA PREPROCESSING Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In machine learning, data preprocessing is the vital and fundamental step to structure the data in a way, so that the model fits perfectly to get accurate results. The data, whether it is big data …