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cost function & loss function
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- 1what is the cost function and loss function....?????‘Loss’ in Machine learning helps us understand the difference between the predicted value & the actual value. The Function used to quantify this loss during the training phase in the form of a single real number is known as the “Loss Function”. These are used in those supervised learning algorithms that use optimization techniques. Notable examples of such algorithms are regression, logistic regression, etc. The terms cost function & loss function are analogous.
Loss function: Used when we refer to the error for a single training example.
Cost function: Used to refer to an average of the loss functions over an entire training dataset. - 2*why* use a cost function?It takes both predicted outputs by the model and actual outputs and calculates how much wrong the model was in its prediction. It outputs a higher number if our predictions differ a lot from the actual values. As we tune our model to improve the predictions, the cost function acts as an indicator of how the model has improved. This is essentially an optimization problem. The optimization strategies always aim at “minimizing the cost function”.
- 3Types of the cost function..?Regression cost Function
Binary Classification cost Functions
Multi-class Classification cost Functions - 4Regression cost Function:Regression models deal with predicting a continuous value for example salary of an employee, price of a car, loan prediction, etc. A cost function used in the regression problem is called “Regression Cost Function”. They are calculated on the distance-based error as follows:
Error = y-y’
Where,
Y – Actual Input
Y’ – Predicted output - 5Other most used regression cost functions..??1.1 Mean Error (ME)
1.2 Mean Squared Error (MSE)
1.3 Mean Absolute Error (MAE) - 6Mean Error .... ???
- 7Mean Squared Error ...??
- 8Mean Absolute Error
- 92. Cost functions for Classification problemsCost functions used in classification problems are different than what we use in the regression problem. A commonly used loss function for classification is the cross-entropy loss. Let us understand cross-entropy with a small example. Consider that we have a classification problem of 3 classes as follows.
Class(Orange,Apple,Tomato)
The machine learning model will give a probability distribution of these 3 classes as output for a given input data. The class with the highest probability is considered as a winner class for prediction.
Output = [P(Orange),P(Apple),P(Tomato)]
The actual probability distribution for each class is shown below.
Orange = [1,0,0]
Apple = [0,1,0]
Tomato = [0,0,1]
- 102.1 Multi-class Classification cost Functions
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- Business Analyst Machine Learning Fichas sobre cost function & loss function, creado por pravin baste el 29/10/2021.
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- pravin baste
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