What is Decision Tree
For many real-life problems, we can build a Decision Tree. In a Decision Tree, we ask a question (decision node), and based on the answer, we either come up with a decision (leaf node) or ask more questions (decision node), and so on.
Here is an example of a duck/not a duck test:
Applying the same logic to the training data allows us to derive one of the most important machine learning algorithms, which can be used for both regression and classification tasks. In this course, we will focus on classification.
The following video illustrates how it works:
In the video above, 'Classes' shows the number of data samples of each class at a node. For example, root node holds all the data samples (4 'cookies', 4 'not cookies'). And the leaf node at the left has only 3 'not cookies'.
With each decision node, we aim to split the training data so that the data points of each class are separated into their own leaf nodes.
A Decision Tree also handles multiclass classification with ease:
And classification with multiple features can also be handled by the decision tree. Now each decision node can split the data using any of the features.
In the video above, the training set is scaled using StandardScaler. It is not necessary for the Decision Tree. It will perform just as well on the unscaled data. But scaling improves the performance of all other algorithms, so it's a good idea to always add the scaling to your preprocessing.
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For many real-life problems, we can build a Decision Tree. In a Decision Tree, we ask a question (decision node), and based on the answer, we either come up with a decision (leaf node) or ask more questions (decision node), and so on.
Here is an example of a duck/not a duck test:
Applying the same logic to the training data allows us to derive one of the most important machine learning algorithms, which can be used for both regression and classification tasks. In this course, we will focus on classification.
The following video illustrates how it works:
In the video above, 'Classes' shows the number of data samples of each class at a node. For example, root node holds all the data samples (4 'cookies', 4 'not cookies'). And the leaf node at the left has only 3 'not cookies'.
With each decision node, we aim to split the training data so that the data points of each class are separated into their own leaf nodes.
A Decision Tree also handles multiclass classification with ease:
And classification with multiple features can also be handled by the decision tree. Now each decision node can split the data using any of the features.
In the video above, the training set is scaled using StandardScaler. It is not necessary for the Decision Tree. It will perform just as well on the unscaled data. But scaling improves the performance of all other algorithms, so it's a good idea to always add the scaling to your preprocessing.
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