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…