# Classification Raport

## What is a classification raport?

A classification report is a performance evaluation tool used in machine learning to assess the quality of predictions from a classification algorithm.

The classification report typically includes the following metrics:

* **Precision**: The ratio of true positive predictions to the total predicted positives. It answers the question, "Of all the instances predicted as positive, how many are actually positive?"
* **Recall (Sensitivity or True Positive Rate)**: The ratio of true positive predictions to the total actual positives. It answers the question, "Of all the actual positive instances, how many were correctly predicted?"
* **F1 Score**: The harmonic mean of precision and recall, providing a single metric that balances the two. It is particularly useful when you need a balance between precision and recall.
* **Support**: The number of actual occurrences of each class in the dataset. It helps in understanding the distribution of the dataset.

***

## **Calculating Model Accuracy**

Accuracy is calculated by dividing the number of correct predictions by the total number of predictions across all classes. In binary classification, it can be expressed as:

Accuracy (ACC) = (TP + TN) / (TP + TN + FP + FN)

Where:

* TP: True Positives (correctly predicted positive instances)
* TN: True Negatives (correctly predicted negative instances)
* FP: False Positives (negative instances predicted as positive)
* FN: False Negatives (positive instances predicted as negative)

***

## Classification report example

In order to use the accuracy and the rest of the metrics, we need to import the class

#### import classification report and accuracy:

```python
from sklearn.metrics import accuraccy, classification_report
```

#### define class lables:

```python
target_names = ['ham', 'spam'] 
```

#### Print accuracy:

```python
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: " + accuracy)

# Output:
# Accuracy: 0.9999415592005299
```

#### Print the report:

```python
report = classification_report(y_test, y_pred, target_names=target_names)
print(report)

# Output:

#                 precision    recall  f1-score   support
#
#              0       1.00      1.00      1.00     51126
#              1       0.99      1.00      0.99       208

#       accuracy                           1.00     51334
#      macro avg       0.99      1.00      1.00     51334
#   weighted avg       1.00      1.00      1.00     51334
```

***

## Full report example:

```python
from sklearn.metrics import accuracy_score, classification_report

target_names = ["Class 1", "Class 2", "Class 3"]

accuracy = accuracy_score(y_test, y_predicted)
report = classification_report(y_test, y_predicted, target_names=target_names)

print(accuracy)
print(report)
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://bata-mladen.gitbook.io/bata-mladen/notes/machine-learning/classification-raport.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.