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# Decision Tree in Machine Learning

A decision tree is a flowchart-like structure in which each internal node represents a test on a feature (e.g. whether a coin flip comes up heads or tails) , each leaf node represents a class label (decision taken after computing all features) and branches represent conjunctions of features that lead to those class labels. The paths from root to leaf represent classification rules. Below diagram illustrate the basic flow of decision tree for decision making with labels (Rain(Yes), No Rain(No)). Decision Tree for Rain Forecasting

Decision tree is one of the predictive modelling approaches used in statistics, data mining and machine learning.

Decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. It is one of the most widely used and practical methods for supervised learning. Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks.

Tree models where the target variable can take a discrete set of values are called classification trees. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. Classification And Regression Tree (CART) is general term for this.

Throughout this post i will try to explain using the examples.

## Data Format

Data comes in records of forms.

`(x,Y)=(x1,x2,x3,....,xk,Y)`

The dependent variable, Y, is the target variable that we are trying to understand, classify or generalize. The vector x is composed of the features, x1, x2, x3 etc., that are used for that task.

Example

```training_data = [
['Green', 3, 'Apple'],
['Yellow', 3, 'Apple'],
['Red', 1, 'Grape'],
['Red', 1, 'Grape'],
['Yellow', 3, 'Lemon'],
]
# Header = ["Color", "diameter", "Label"]
# The last column is the label.
# The first two columns are features.```

## Approach to make decision tree

While making decision tree, at each node of tree we ask different type of questions. Based on the