Machine Learning Classification Model | Train test split | Kunaal Naik

In this video, Kunaal Naik, a Data Scientist and corporate trainer, will explain why this step is so crucial and how it can help you create more accurate and reliable models.
As you may know, KNIME is a powerful data analytics platform that allows you to build machine learning models for a variety of use cases. When it comes to classification models, one of the most important steps in the modeling process is splitting your data into training and testing sets.
#1: The train-test split allows you to evaluate your model's performance on data that it has not seen before. When you train your model on a dataset, it can sometimes "overfit" the data, meaning that it becomes too specialized to the training set and doesn't generalize well to new data. By setting aside a portion of your data for testing, you can get a better sense of how your model will perform in the real world.
#2: The train-test split helps you optimize your model's parameters. By testing your model on a separate dataset, you can experiment with different settings and see how they impact the model's performance. This can help you fine-tune your model and improve its accuracy.
#3: train-test split is a fundamental practice in machine learning and data science. Most experts agree that it's essential to split your data into training and testing sets if you want to create models that are reliable and trustworthy.
So if you're building a classification model using KNIME, be sure to include a train-test split in your workflow. It will help you create better models, optimize your parameters, and ensure that your results are accurate and reliable.
In this series, learn how to use machine learning codes and ML without coding to grow your career in the field of data science. Stay tuned for more tutorials and tips on advanced topics in Machine Learning and Data Science.
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