Implementation of Decision Tree Classification Algorithm in Python | IRIS | Machine Learning | 2021 смотреть онлайн

Google Colab: https://colab.research.google.com/
Iris Dataset Link: https://drive.google.com/file/d/11Wypj0cnLQfI1508rwPjSo8GroRGX6Z2
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Decision tree learning is one of the predictive modelling approaches used in statistics, data mining and machine learning. It uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity.

In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data (but the resulting classification tree can be an input for decision making). This page deals with decision trees in data mining.
Source: https://en.wikipedia.org/wiki/Decision_tree_learning

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