What is Machine Learning?
Machine learning is a method of teaching computers to learn from data, without being explicitly programmed.
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Machine learning is a method of teaching computers to learn from data, without being explicitly programmed.
Read more..
In a supervised learning problem, there is an explicit, pre-defined target variable which has known labels.
Structured Data is data that has a clear and pre-defined schema. Unstructured Data encompasses the wide spectrum of data that does not fall within the structured category.
Overfitting occurs when a model fails to perform at a similar level of accuracy on data that was not used in the training process compared to data that it was explicitly built on.
Underfitting occurs when a model fails to capture the complexity of the training data and thus is a poor representation of the relationship between the features and the target.
The bias/variance tradeoff refers to the challenge of finding a model that both performs at a high level of accuracy on the data on which it is trained (bias) while at the same time generalizes well to unseen data (variance)
Overfitting can be mitigated by collecting more training data, using regularization and so on
Underfitting can be mitigated using improved feature selection, choosing an appropriate algorithm, decreasing regularization
A model that is underfit will produce evaluation metrics that are poor on the training data alone, such as high RMSE or misclassification rate.
The curse of dimensionality refers to the potential dangers associated with modeling from a dataset that has a large number of features.
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