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Machine Learning Introduction
Ishaan Javali edited this page Jul 1, 2019
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Machine learning is a branch of Artificial Intelligence (AI) that is the study of algorithms and models that train on data sets to improve their performance in predicting values for new data. Machine learning can be implemented through algorithms and neural networks, both of which can be used for supervised and unsupervised learning.
- Underfitting - The model is too simple and makes too many assumptions about the data and cannot find the underlying trend. High variance, low bias.
- Overfitting - The model is trained too well on the data and “memorizes” it. Therefore, it captures the noise in the dataset, making it less accurate. Low bias, high variance.
- Generalization - Refers to an algorithm’s ability to be effective across a range of inputs.
- Bias - Difference between the expected output and the average prediction of the model.
- Variance - The amount by which the model’s predictions for a data point vary.
- Parameters - Parameters are the data values that are being given to the model to train on.
- Hyperparameters - Hyperparameters are the features of a model that can be tuned to provide optimal results.