Loss functions in Python are an integral part of any machine learning model. These functions tell us how much the predicted output of the model differs from the actual output.

There are multiple ways of calculating this difference. In this tutorial, we are going to look at some of the more popular loss functions.

**We are going to discuss the following four loss functions in this tutorial.**

- Mean Square Error
- Root Mean Square Error
- Mean Absolute Error
- Cross-Entropy Loss

Out of these 4 loss functions, the first three are applicable to regressions and the last one is applicable in the case of classification models.

Table of Contents

## Implementing Loss Functions in Python

Let’s look at how to implement these loss functions in Python.

### 1. Mean Square Error (MSE)

Mean square error (MSE) is calculated as the average of the square of the difference between predictions and actual observations. Mathematically we can represent it as follows :

**Python implementation for MSE is as follows :**

```
import numpy as np
def mean_squared_error(act, pred):
diff = pred - act
differences_squared = diff ** 2
mean_diff = differences_squared.mean()
return mean_diff
act = np.array([1.1,2,1.7])
pred = np.array([1,1.7,1.5])
print(mean_squared_error(act,pred))
```

Output :

```
0.04666666666666667
```

**You can also use mean_squared_error from sklearn to calculate MSE. Here’s how the function works**:

```
from sklearn.metrics import mean_squared_error
act = np.array([1.1,2,1.7])
pred = np.array([1,1.7,1.5])
mean_squared_error(act, pred)
```

Output :

```
0.04666666666666667
```

### 2. Root Mean Square Error (RMSE)

Root Mean square error (RMSE) is calculated as the square root of Mean Square error. Mathematically we can represent it as follows :

**Python implementation for RMSE is as follows:**

```
import numpy as np
def root_mean_squared_error(act, pred):
diff = pred - act
differences_squared = diff ** 2
mean_diff = differences_squared.mean()
rmse_val = np.sqrt(mean_diff)
return rmse_val
act = np.array([1.1,2,1.7])
pred = np.array([1,1.7,1.5])
print(root_mean_squared_error(act,pred))
```

Output :

```
0.21602468994692867
```

**You can use mean_squared_error from sklearn to calculate RMSE as well. Let’s see how to implement the RMSE using the same function:**

```
from sklearn.metrics import mean_squared_error
act = np.array([1.1,2,1.7])
pred = np.array([1,1.7,1.5])
mean_squared_error(act, pred, squared = False)
```

Output :

```
0.21602468994692867
```

If the parameter ‘*squared*‘ is set to **True** then the function returns **MSE **value. If set to **False, **the function returns **RMSE **value.

### 3. Mean Absolute Error (MAE)

Mean Absolute Error (MAE) is calculated as the average of the absolute difference between predictions and actual observations. Mathematically we can represent it as follows :

**Python implementation for MAE is as follows :**

```
import numpy as np
def mean_absolute_error(act, pred):
diff = pred - act
abs_diff = np.absolute(diff)
mean_diff = abs_diff.mean()
return mean_diff
act = np.array([1.1,2,1.7])
pred = np.array([1,1.7,1.5])
mean_absolute_error(act,pred)
```

Output :

```
0.20000000000000004
```

**You can also use mean_absolute_error from sklearn to calculate MAE. **

```
from sklearn.metrics import mean_absolute_error
act = np.array([1.1,2,1.7])
pred = np.array([1,1.7,1.5])
mean_absolute_error(act, pred)
```

Output :

```
0.20000000000000004
```

### 4. Cross-Entropy Loss Function in Python

Cross-Entropy Loss is also known as the **Negative Log Likelihood**. This is most commonly used for classification problems. A classification problem is one where you classify an example as belonging to one of more than two classes.

Let’s see how to calculate the error in case of a binary classification problem.

Let’s consider a classification problem where the model is trying to classify between a dog and a cat.

**The python code for finding the error is given below.**

```
from sklearn.metrics import log_loss
log_loss(["Dog", "Cat", "Cat", "Dog"],[[.1, .9], [.9, .1], [.8, .2], [.35, .65]])
```

Output :

```
0.21616187468057912
```

**We are using the log_loss method from sklearn. **

The first argument in the function call is the** list of correct class labels** for each input. The second argument is a** list of probabilities as predicted** by the model.

The probabilities are in the following format :

```
[P(dog), P(cat)]
```

## Conclusion

This tutorial was about Loss functions in Python. We covered different loss functions for both regression and classification problems. Hope you had fun learning wiht us!