https://s3-us-west-2.amazonaws.com/secure.notion-static.com/d4e66190-e5e0-4216-8b08-9b4c210cdd08/MachineLearningCheatsheet.pdf

https://storage.ning.com/topology/rest/1.0/file/get/1211570060?profile=original

https://s3-us-west-2.amazonaws.com/secure.notion-static.com/b82b2c4f-b495-41d4-ab22-b021049ee0ca/AnotherMachineLearningCheatsheet.pdf

The Machine Learning Process:

  1. Import the model.
  2. Instantiate the model with the hyperparameters of interest.
  3. Fit the model to the training data.
  4. Predict on the test data.
  5. Score the model by comparing the predictions to the actual values.

The plots show training points in solid colors and testing points semi-transparent. The lower right shows the classification accuracy on the test set.

https://s3-us-west-2.amazonaws.com/secure.notion-static.com/b2ae13b4-31df-46a8-8e5d-217b2c1e71bf/Untitled

A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets.

Particularly in high-dimensional spaces, data can more easily be separated linearly and the simplicity of classifiers such as naive Bayes and linear SVMs might lead to better generalization than is achieved by other classifiers.