Supervised Learning correct answers Training of a ML model using datasets with labels
Unsupervised Learning correct answers Training of a ML model without labeled datasets
Training Set correct answers Set of data with labels and features used to teach the ML model/determine its parameters
...
Supervised Learning correct answers Training of a ML model using datasets with labels
Unsupervised Learning correct answers Training of a ML model without labeled
datasets
Training Set correct answers Set of data with labels and features used to teach the ML
model/determine its parameters
Validation Set correct answers Set used during training to fine tune the model's
parameters/determine its hyperparameters & evaluate the model's performance
Testing Set correct answers Used as an "exam" to validate that the ML model is
actually learning
K-Fold Cross Validation correct answers Used to split the training set into k smaller
sets & used to find the best hyperparameter for the number of nearest neighbors. Useful
when data set small
Underfitting correct answers When you had the opportunity to learn something & didn't.
Ex:
Shallow decision tree
Overfitting correct answers When you pay too much attention to the idiosyncrasies of
the training data & aren't able to generalize well Ex: Deep decision tree
Precision correct answers Of all the X's that you found, how many of them were
actually X's?
Good to use when you want to minimize false positives (it's bad to classify something as
positive when it's not)
Recall correct answers Of all the X's that were out there, how many did you find?
Good to use when you want to minimize false negatives (it's bad to classify something
as negative when it's not)
R = I/T OR R = TP/(TP+FN) correct answers Recall Formula
P = I/S OR P = TP/(TP+FP) correct answers Precision Formula
F-Score correct answers Single number that tell us the quality of the solution
Harmonic mean rather than arithmetic
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