Why do we split any available data into training and test data sets?

Separating data into training and testing sets is an important part of evaluating data mining models. By using similar data for training and testing, you can minimize the effects of data discrepancies and better understand the characteristics of the model.

What is a common reason for an ML model that works well in training but fails in production?

Representative training data is also key: If your training data doesn’t reflect the actual datasets your model will encounter, you may end up with a model that won’t perform once you’ve reached testing or production. Another issue that can occur during training is overfitting and underfitting.

What does the training data which is a smaller chunks than the test data help you find?

The smaller the training data set, the lower the test accuracy, while the training accuracy remains at about the same level.

What is the ratio of training and testing data?

Generally, the training and validation data set is split into an 80:20 ratio. Thus, 20\% of the data is set aside for validation purposes.

Why should the data be partitioned into training and validation sets what will the training set be used for what will the validation set be used for?

Why are Training, Validation, and Holdout Sets Important? Partitioning data into training, validation, and holdout sets allows you to develop highly accurate models that are relevant to data that you collect in the future, not just the data the model was trained on.

What is the significance of training and test dataset in predictive modeling?

The Test dataset provides the gold standard used to evaluate the model. It is only used once a model is completely trained(using the train and validation sets).

Why does an ML model performance degrade in production?

Lack of Monitoring: The model in production needs to be monitored on regular basis. The data might change with time, the model that was performing well earlier, the performance will decrease with time. The response variable or the independent variable might change over time that may impact the predictors.

What is the reason behind the better performance of ensemble models?

There are two main reasons to use an ensemble over a single model, and they are related; they are: Performance: An ensemble can make better predictions and achieve better performance than any single contributing model. Robustness: An ensemble reduces the spread or dispersion of the predictions and model performance.

Why training data is more than test data?

Let us assume that both training and test data samples come from the same distribution i.e. there are common patterns in both. If, while testing, you present some examples having complex patterns which are different from the ones model is trained on, then there is a high probability of the output being incorrect.

What is machine learning training?

A machine learning training model is a process in which a machine learning (ML) algorithm is fed with sufficient training data to learn from. The ability of ML models to process large volumes of data can help manufacturers identify anomalies and test correlations while searching for patterns across the data feed.

Why training data set must be larger than testing data set?

Larger test datasets ensure a more accurate calculation of model performance. Training on smaller datasets can be done by sampling techniques such as stratified sampling. It will speed up your training (because you use less data) and make your results more reliable.

Which proportion of training and testing data is most widely accepted?

As mentioned by others before, the ratio 80:20 (Train : Test) would be the most commonly used also referred to as the Pareto principle (LINK: https://en.wikipedia.org/wiki/Pareto_principle).