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…
The cost function in the context of decision trees refers to a metric used to determine the “quality” of a split at any given node. Depending on the nature of the task (classification or regression), different cost functions are used: Classification: Gini Impurity: It measures the disorder in a set….
The decision tree algorithm is one of the 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 a tree…
Naive Bayes is a linear classifier using Bayes Theorem and strong independence condition among features. Given a data set with n features represented by Naive Bayes states the probability of output: Y from features F_i is, Bayes theorem states that: Logistic regression is a linear classification method that learns the probability…
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….
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…
Gradient Descent is an optimization algorithm used to find the values of the parameters of any function that minimizes the cost function. The average difference of the squares of all the predicted values of y and the actual values of y is called a Cost Function. It is also called…
Linear regression is a supervised machine learning algorithm used for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation. I would like to say it is the starting point of anyone’s ML journey! Linear regression is the simplest and most widely…
There are the five major assumptions: 1. Linear relationship: There should be a linear and additive relationship between the dependent (Y) variable and the independent (X –> x1,x2,x3,…) variable(s). A linear relationship suggests that a change in response Y due to one unit change in x1 is constant, regardless of…
Unsupervised learning is a class of ML where we do not have any clue about the target data. We have to approach the problem by clustering the given unlabeled data based on the relationship between the input variables. We have to analyze the data; find patterns and relationships among them….