Logistic Regression
Dr. Ashish Tendulkar
Machine Learning Practice
IIT Madras
Logistic Regression (also called Logit Regression) is commonly used to estimate the probability that an instance belongs to a particular class.
Introduction
- If the estimated probability is greater than 50%, then the model predicts that the instance belongs to that class (called the positive class, labeled “1”), or else it predicts that it does not (i.e., it belongs to the negative class, labeled “0”).
- This makes it a binary classifier.
Loading the dataset
Cleveland Heart-disease dataset
Attribute Information:
- Age (in years)
- Sex (1 = male; 0 = female)
- cp -chest pain type
- trestbps - resting blood pressure (anything above 130-140 is typically cause for concern)
- chol-serum cholestoral in mg/dl (above 200 is cause for concern)
- restecg - resting electrocardiographic results (0 = normal;1 = having ST-T wave abnormality; 2 = showing probable or definite left ventricular hypertrophy by Estes' criteria)
- thalach-maximum heart rate achieved
Loading the dataset
Cleveland Heart-disease dataset
Attribute Information:
- exang - exercise induced angina (1 = yes; 0 = no)
- oldpeak - depression induced by exercise relative to rest
- slope - slope of the peak exercise ST segment (1 = upsloping; 2 = flat Value; 3 = downsloping)
- ca - number of major vessels (0-3) colored by flourosopy
- thal - (3 = normal; 6 = fixed defect; 7 = reversable defect
- num (target) - diagnosis of heart disease (angiographic disease status)( 0: < 50% diameter narrowing ; 1: > 50% diameter narrowing)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119767/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119787/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119790/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119799/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119810/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119865/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119886/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119890/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119924/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119926/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119938/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119940/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119942/pasted-from-clipboard.png)
Visualizing dataset and features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119944/pasted-from-clipboard.png)
Understanding the correlation between Input features
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119959/pasted-from-clipboard.png)
Confusion Matrix
A confusion matrix is a summary of prediction results on a classification problem.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119968/pasted-from-clipboard.png)
Confusion Matrix
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9119976/pasted-from-clipboard.png)
Hyperparameter tuning with RandomizedSearchCV and GridSearchCV
RandomizedSearchCV
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9120030/pasted-from-clipboard.png)
Hyperparameter tuning with RandomizedSearchCV and GridSearchCV
GridSearchCV
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9120047/pasted-from-clipboard.png)
With Pipeline
![](https://s3.amazonaws.com/media-p.slid.es/uploads/2010658/images/9120055/pasted-from-clipboard.png)
Logistic Regression
By Debajyoti Biswas
Logistic Regression
- 95