Support Vector Machines

A support vector machine (SVM) is a supervised machine learning model which can be used for both classification and regression. But they have been extensively used for solving complex classification problems such as image recognition, voice detection etc. SVM algorithm outputs an optimal hyperplane that best separates the tags. The hyperplane is a boundary that…

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Random Forest

Random Forest algorithm is a supervised classification algorithm. It is a tree-based algorithm comprised of several decision trees. The difference between the Random Forest algorithm and the decision tree algorithm is that in Random Forest, the processes of finding the root node and splitting the feature nodes will run randomly….

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Gini Index

In Decision Tree, the major challenge is the identification of the attribute for the root node in each level. This process is known as attribute selection. There are two popular attribute selection measures: Gini Index Information Gain Gini Index calculates the amount of probability of a specific feature that is…

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Decision Trees

The decision tree algorithm is one of the best and most widely used algorithms in Machine Learning. It is a supervised learning algorithm. A decision tree uses a tree-like model to make predictions. It resembles an upside-down tree. A decision tree builds classification or regression models in the form of…

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Ridge and Lasso Regression

Regularization is a process used to create an optimally complex model. A model should be as simple as possible. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression. Linear regression is the simplest supervised machine learning…

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Naive Bayes

Naive Bayes is a very popular Supervised Classification algorithm. This algorithm is called “Naive” because it makes a naive assumption that each feature is independent of other features. It is near to impossible to find such data sets in real life. Bayes’ theorem is the base for Naive Bayes Algorithm….

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ROC Curve and AUC

ROC curves and AUC are used to measure performance in machine earning. They are the most widely used evaluation metrics for checking any classification model’s performance. It tells how much the model is capable of distinguishing between classes. ROC (Receiver Operator Characteristic Curve) is a probability curve and AUC represents the…

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Logistic Regression

Logistic Regression is a supervised classification algorithm that is used to predict the probability of a categorical dependent variable using a given set of independent variables. It is a predictive analysis algorithm and based on the concept of probability. The most common use of logistic regression models is in binary classification problems. Some…

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