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Commit890f3ec

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GITBOOK-68: Data Sets & Data Split
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‎docs/machine-learning/1.-data-sets.md‎

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#1. Data Sets
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---
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description:Import or Create Sample Data
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---
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#1. Data Sets
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<figure><imgsrc="../.gitbook/assets/image (144).png"alt=""width="211"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (318).png"alt=""width="527"><figcaption></figcaption></figure>
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1. Click on_**Data Sets**_ in the_**Machine Learning**_ category.
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<figure><imgsrc="../.gitbook/assets/image (145).png"alt=""width="563"><figcaption></figcaption></figure>
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2._**Load Type**_: You can load sample data (_**Load Data**_) or generate data (_**Create Data**_).
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3._**Allocate to**_: Specify variable names to assign to the data.

‎docs/machine-learning/2.-data-split.md‎

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#2. Data Split
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description:Divide Your Data into Training and Testing Data
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#2. Data Split
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<figure><imgsrc="../.gitbook/assets/image (146).png"alt=""width="211"><figcaption></figcaption></figure>
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<figure><imgsrc="../.gitbook/assets/image (320).png"alt=""width="516"><figcaption></figcaption></figure>
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1. Click on_**Data Split**_ in the_**Machine Learning**_ category.
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<figure><imgsrc="../.gitbook/assets/image (321).png"alt=""width="563"><figcaption></figcaption></figure>
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2._**Input Data**_: Choose whether the target data is included in the input data. If it is, select_**Feature Data**_ and_**Target Data**_ separately. You can also select specific columns from one dataset using the_**funnel icon**_.
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2._**Input Data**_: Choose whether the target data is included in the input data. If it is, select_**Feature Data**_ and_**Target Data**_ separately. You can also select specific columns from one dataset using the funnel icon.
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3._**Test Size**_: Select the percentage of input data to use for testing purposes.
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4._**Random State**_: Generate the same random state, ensuring consistent data splits each time. (If not set, data will be randomly split differently each time.)
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5._**Shuffle**_: Shuffle the data randomly to prevent the model from relying on the order of the data, thereby reducing bias and improving generalization performance.

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