Sivasankar Blog: Common used loss functions in Data Science

Thursday, June 6, 2019

Loss Functions

A function that evaluates the goodness of our models
Mainly evaluates the difference between: $Y - \hat{Y}$ (Act - Pred) by assigning some kind of function
Many are already implemented in python's ML library sklearn (EG: RMSE, binary/cross entropy loss etc

M e a n S q u a r e d E r r o r = \frac{1}{N} \sum_{i = 1}^{n} (Y_{i} - \hat{Y_{i}})^{2}

R o o t M e a n S q u a r e d E r r o r = \sqrt{\frac{1}{N} \sum_{i = 1}^{n} (Y_{i} - \hat{Y_{i}})^{2}}

M e a n A b s o l u t e E r r o r = \frac{1}{N} \sum_{i = 1}^{n} | (Y_{i} - \hat{Y_{i}}) |

M e a n A b s o l u t e P e r c e n t E r r o r = \frac{100}{N} * \sum_{i = 1}^{N} | \frac{Y_{i} - \hat{Y_{i}}}{Y_{i}} |

Y_{i}

= Target value for

i_{t h}

example

\hat{Y_{i}}

= Predicted value for

i_{t h}

example

C r o s s E n t r o p y L o s s (L o g L o s s) = - \frac{1}{n} \sum_{i = 1}^{n} Y_{i} \log ({\hat{Y}}_{i}) + (1 - Y_{i}) l o g (1 - {\hat{Y}}_{i})

Y_{i}

= Target value for

i_{t h}

example

\hat{Y_{i}}

= Predicted value for

i_{t h}

example

ROC, AUC

Precision and Recall

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