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Confusion Matrix | ML | AI | sklearn.metrics.classification_report | Classification Report - P8
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#technologycult #confusionmatrix #pythonformachinelearning #classificationreport
Topics to be covered -
Precision, Recall and F1 Score using
Classification Report
All Playlist of this youtube channel
=============================
1. Data Preprocessing in Machine Learning
2. Confusion Matrix in Machine Learning, ML, AI
3. Anaconda, Python Installation, Spyder, Jupyter Notebook, PyCharm, Graphviz
4. Cross Validation, Sampling, train test split in Machine Learning
5. Drop and Delete Operations in Python Pandas
6. Matrices and Vectors with python
7. Detect Outliers in Machine Learning
8. TimeSeries preprocessing in Machine Learning
9. Handling Missing Values in Machine Learning
10. Dummy Encoding Encoding in Machine Learning
11. Data Visualisation with Python, Seaborn, Matplotlib
12. Feature Scaling in Machine Learning
13. Python 3 basics for Beginner
14. Statistics with Python
15. Sklearn Scikit Learn Machine Learning
16. Python Pandas Dataframe Operations
17. Linear Regression, Supervised Machine Learning
18 Interview Questions on Machine Learning, Artificial Intelligence, Python Pandas and Python Basics
19. Jupyter Notebook Operations
Code Starts Here
==============
y = [1,0,1,1,2,2,0]
y_pred = [1,1,1,1,1,1,2]
precision_score(y,y_pred,average=None)
recall_score(y,y_pred,average=None)
f1_score(y,y_pred,average=None)
import pandas as pd
logit=LogisticRegression()
mat = confusion_matrix(y,y_pred)
classification_report(y,y_pred)
print(classification_report(y,y_pred))
target_names = ['Class A','Class B','Class C']
print(classification_report(y,y_pred,target_names=target_names))
print(classification_report(y,y_pred,labels=[0,1,2]))
print(classification_report(y,y_pred,labels=[0,1,2,3]))
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