Data splitting technique to fit any Machine Learning Model

This aims to be a short 4-minute article to introduce you guys with Data splitting technique and its importance in practical projects.

Ethically, it is suggested to divide your dataset into three parts to avoid overfitting and model selection bias called -

1. Training set (Has to be the largest set)
2. Cross-Validation set or Development set or Dev set
3. Testing Set

The test set can be sometimes omitted too. It is meant to get an unbiased estimate of algorithms performance in the real world. People who divide their dataset into just two parts usually call their Dev set the Test set.

We try to build a model upon training set then try to optimize hyperparameters on the dev set as much as possible then after our model is ready, we try and evaluate the testing set.

# Training Set:

The sample of data used to fit the model, that is the actual subset of the dataset that we use to train the model (estimating the weights and biases in the case ofNeural Network). The model observes and learns from this data and optimize its parameters.

# Cross-Validation Set:

We select the appropriate model or the degree of the polynomial (if using regression model only) by minimizing the error on the cross-validation set.

# Test set:

The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset. It is only used once the model is completely trained using the training and validation sets. Therefore test set is the one used to replicate the type of situation that will be encountered once the model is deployed for real-time use.

The test set is generally what is used to evaluate different models in competitions of Kaggle or Analytics Vidhya . Generally in a Machine Learning hackathon, the cross-validation set is released along with the training set and the actual test set is only released when the competition is about to close, and it is the score of the model on the Test set that decides the winner.

# How to decide the ratio of splitting the dataset?