---
title: "Discussion on \"Train Test Split: Importance, Use Cases, and Practical Implementation\" | Hashnode"
description: "Discussion on \"Train Test Split: Importance, Use Cases, and Practical Implementation\". Machine learning models are trained on data to make predictions or classifications on new data. However, if the same data is used for training and testing the model, the model will memorize the data and perform poorly on new data. To avoid this probl..."
url: "https://hashnode.com/posts/train-test-split-importance-use-cases-and-practical-implementation/6443df5261f861a859a5dc1e"
---

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# [Train Test Split: Importance, Use Cases, and Practical Implementation](https://rhythmblogs.hashnode.dev/train-test-split-importance-use-cases-and-practical-implementation?utm_source=hashnode&utm_medium=feed)

Machine learning models are trained on data to make predictions or classifications on new data. However, if the same data is used for training and testing the model, the model will memorize the data and perform poorly on new data. To avoid this probl...

Apr 22, 2023 · 3 min read · rhythmblogs.hashnode.dev  
Updated Nov 17, 2023  
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## Written by

[Rhythm Rawat](https://hashnode.com/@Rhythm1821)

@Rhythm1821 · India

  
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Coding to the Rhythm

Machine learning enthusiast with a strong emphasis on computer vision and deep learning. Skilled in using well-known machine learning frameworks like TensorFlow , scikit-learn and PyTorch for effective model development. Familiarity with transfer learning and fine-tuning pre-trained models to achieve better results with limited data. Proficient in Python, Machine Learning, SQL, and Flask.

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