When we make a prediction for a binary classification problem, there are only four possible outcomes.  

> Note
>
> In the image above, the actual values are in descending order, and the predicted values are in ascending. This is the layout used in the Scikit-learn for the confusion matrix(learned later in the chapter). You may encounter different layouts in other visualizations, but nothing apart from the order changes.

We call those outcomes **True Positive(TP)**, **True Negative(TN)**, **False Positive(FP)**, **False Negative(FN)**, where True/False stands for whether the prediction is correct and Positive/Negative stands for what is the predicted class 1 or 0.  
So we can make two types of errors: False Positive and False Negative.  
The case of False Positive prediction is also called a **Type 1 Error**.  
And the case of False Negative prediction – **Type 2 Error**.

### Confusion Matrix
The first way to look at the model's performance is to organize the predictions into a **confusion matrix** like this:

You can build a confusion matrix in Python using the `confusion_matrix()` from `sklearn`.

```python
from sklearn.metrics import confusion_matrix

conf_matrix = confusion_matrix(y_true, y_pred)
```
And, for better visualization, you can use the `heatmap()` function of `sns`(seaborn).
```ptyhon
sns.heatmap(conf_matrix)
```

Here is an example of calculating the confusion matrix for a Random Forest prediction on the titanic dataset:

import pandas as pd 
import seaborn as sns
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Read the data and assign the variables
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/titanic.csv')
X, y = df.drop('Survived', axis=1), df['Survived']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
# Build and train a Random Forest and predict target for a test set
random_forest = RandomForestClassifier().fit(X_train, y_train)
y_pred = random_forest.predict(X_test)
# Build a confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred)
sns.heatmap(conf_matrix, annot=True)

We can also plot the percentages instead of the instances count using the `normalize` parameter:
```python
conf_matrix = confusion_matrix(y_true, y_pred, normalize='all')
```

import pandas as pd 
import seaborn as sns
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Read the data and assign the variables
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/titanic.csv')
X, y = df.drop('Survived', axis=1), df['Survived']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
# Build and train a Random Forest and predict target for a test set
random_forest = RandomForestClassifier().fit(X_train, y_train)
y_pred = random_forest.predict(X_test)
# Build a confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred, normalize='all')
sns.heatmap(conf_matrix, annot=True)

In machine learning, classification is used in predictive modeling to assign input data with a class label. Sounds difficult? Don't worry! Let's cope with this! Welcome to the ML!

Classification with Python

Confusion Matrix

Confusion Matrix