Define Loss function in the simplest way possible
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Answers ( 12 )
In simplest terms, loss function is the amount by which your predictions are deviating from
your actual data
loss function can be considered as difference between actual and predicted values. primary goals of machine learning model is to minimize the loss function
It tells us how far off the result your model produced is from the expected result –it indicates the magnitude of error your model made on its prediction.
In supervised machine learning algorithms, we want to minimize the error for each training example during the learning process. This is done using some optimization strategies like gradient descent. And this error comes from the loss function.
Loss function is the amount by which the predicted values differ from the actual values. In regression the loss functions that are generally used are RSME, MSE, least squre error In classification problem, the loss function that is used in log loss, softmax cross entropy loss.
Loss function actually tells us how much information we are losing during the prediction. It is basically the difference between actual and predicted target values. We use optmization techniques like gradient descent to minimize the loss function.
In simple terms, loss function is a function which tells how much are the values on our predicted line are deviating from the original values.
Loss Function represents the difference between predicted values and the actual values.
Loss functions show the difference between the original values and the predicted values. It tells us if our model is performing good or bad. Based on these values we can improve our model and try to minimize the loss function. Example: In regression, the loss functions that are generally used are RSME, MSE, etc.
It is a method of evaluating how well our algorithm models our dataset. It gives a higher value if our prediction is totally off otherwise gives a lower value.
Typically a loss function is used for parameter estimation, and the event in question is some function of the difference between estimated and true values for an instance of data.
Let’s say you are on the top of a hill and need to climb down. How do you decide where to walk towards?
Here’s what I would do:
1. Look around to see all the possible paths
2. Reject the ones going up. This is because these paths would actually cost me more energy and make my task even more difficult
3. Finally, take the path that I think has the most slope downhill
This intuition that I just judged my decisions against? This is exactly what a loss function provides.
A loss function maps decisions to their associated costs.
Deciding to go up the slope will cost us energy and time. Deciding to go down will benefit us. Therefore, it has a negative cost.
In supervised machine learning algorithms, we want to minimize the error for each training example during the learning process. This is done using some optimization strategies like gradient descent. And this error comes from the loss function.
A loss function is a measure of how good a prediction model does in terms of being able to predict the expected outcome.
A most commonly used method of finding the minimum point of function is “gradient descent”.