Key Concepts of Machine Learning-2| Day (3/45) | A2Z ML | Mohd Saqib

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In this blog, we will discussed some important questions may arise before actually starting the modeling.

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Day 3

Index:
- Why split data (Training vs Validation vs Testing)?
- Cross-validation: Evaluating validation data?
- Regression vs Classification vs Clustering
- Metrics to evaluation of Testing data

Why split data (Training vs Validation vs Testing)?

Typically the whole data is divided into three parts: training, validation and testing. The training data is used to train the model and adjust its parameters. The validation data is used to evaluate the model’s performance during the training process and tune the hyperparameters. The testing data is used to evaluate the final performance of the model after it has been trained and validated.

It is important to note that the ratio of the data split between these three sets may vary depending on the size of the dataset and the specific use case. For example, if the dataset is small, it may be necessary to use a smaller validation and testing set, whereas if the dataset is large, the validation…