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ICT706 Decision Tree With Python

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Question:

Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

Learning Objectives

In this lab you will learn to:

1.build a decision tree from the dataset,

Anaconda has scikit-learn package.

We will use scikit-learn package for decision tree analysis and prediction.

If we want to use 80% of original dataset as the training set, and 20% as the test set, which statement can help to do the right sampling?

Visualize the tree trained on your training set?

We first set a seed which is a number used by Python's random number generator. The major advantage of setting a seed is that we can get the same sequence of random numbers whenever we use the same seed.

Cite This Work

To export a reference to this article please select a referencing stye below:

My Assignment Help. (2020). Decision Tree With Python. Retrieved from https://myassignmenthelp.com/free-samples/ict706-data-analytics/decision-tree-analysis-and-prediction.html.

My Assignment Help (2020) Decision Tree With Python [Online]. Available from: https://myassignmenthelp.com/free-samples/ict706-data-analytics/decision-tree-analysis-and-prediction.html
[Accessed 26 July 2021].

My Assignment Help. 'Decision Tree With Python' (My Assignment Help, 2020) <https://myassignmenthelp.com/free-samples/ict706-data-analytics/decision-tree-analysis-and-prediction.html> accessed 26 July 2021.

My Assignment Help. Decision Tree With Python [Internet]. My Assignment Help. 2020 [cited 26 July 2021]. Available from: https://myassignmenthelp.com/free-samples/ict706-data-analytics/decision-tree-analysis-and-prediction.html.


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