Learning Curves: Machine Learning Made Simple

This is a video on Learning Curves. Learning Curves are a very important diagnostic tool in Machine Learning. They help you understand how well your model has actually learnt from the data, and how good the fit is. This is crucial. We use this alongside the fit of the data, to decide the best model for our Machine Learning Solutions.

Overview:
A learning curve is a plot of model learning performance over experience or time/training size.
Learning curves are a widely used diagnostic tool in machine learning for algorithms that learn from a training dataset incrementally. We can use them to analyze how our model performs when we add more data to the training data.
The model can be evaluated on the training dataset and on a hold out validation dataset after each update.
Learning curves of models during training can be used to diagnose problems with learning, such as an underfit or overfit model, or whether the training and validation datasets are suitably representative.


Formal:
In machine learning, a learning curve (or training curve) plots the optimal value of a model's loss function for training set against this loss function evaluated on a validation data set with the same parameters as produced the optimal function. It is a tool to find out how much a machine model benefits from adding more training data and whether the estimator suffers more from a variance error or a bias error. If both the validation score and the training score converge to a value that is too low with the increasing size of the training set, it will not benefit much from more training data.

The machine learning curve is useful for many purposes including comparing different algorithms, choosing model parameters during design, adjusting optimization to improve convergence, and determining the amount of data used for training.

In the machine learning domain, there are two implications of learning curves differing in the x-axis of the curves, with experience of the model graphed either as the number of training examples used for learning or the number of iterations used in training the model.

About Machine Learning Made Simple:
Machine Learning Made Simple is a playlist that aims to break down complex Machine Learning and AI topics into digestible videos. With this playlist, you can dive head first into the world of ML implementation and/or research. Feel free to drop any feedback you might have down below.

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