XGBoost, short for “Extreme Gradient Boosting,” is a machine learning algorithm that has taken the data science world by storm. It has been widely recognized for its exceptional performance in various competitions and real-world applications. In this blog post, we’ll explore what makes XGBoost so remarkable and why it’s a…
Boosting is a powerful ensemble learning technique that can significantly enhance the performance of machine learning models. Two popular boosting algorithms are AdaBoost and Gradient Boosting, each with its unique strengths and applications. In this blog post, we’ll take a closer look at both AdaBoost and Gradient Boosting to understand…
Bagging and boosting are both ensemble techniques in machine learning, where multiple models are used together to achieve better performance than any single model alone. Bagging (Bootstrap Aggregating): Improve the stability and accuracy of machine learning algorithms by combining the results of multiple models. Random subsets (or samples) of the…
Random Forest is a supervised algorithm and an ensemble learning method that constructs a multitude of decision trees during training and outputs the class that is the mode of the classes (classification) or the mean prediction (regression) of the individual trees. It is known for its flexibility and is used…
A probability distribution is a way to describe how likely different outcomes are in an experiment. It tells us what outcomes are possible and how likely they are to occur. In other words, it’s a mathematical function that provides the probabilities of occurrence of different possible outcomes. Types of Probability…
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…
Bootstrapping is a resampling method that involves taking repeated samples (called ‘bootstrap samples’) from a dataset with replacement. It is used to estimate the distribution of a statistic and to calculate confidence intervals and significance tests. Here is the basic procedure: Draw a Sample: Randomly select n observations from the…
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…