Logistic regression is a supervised machine learning algorithm which can be used for performing classification problems. It calculates the probability that a given value belongs to a specific class. If the probability is more than 50%, it assigns the value in that particular class else if the probability is less than 50%, the value is assigned to the other class. Therefore, we can say that logistic regression acts as a binary classifier.

##### Theoretical Explanation Part 1

##### Theoretical Explanation Part 2

##### Theoretical Explanation Part 3

##### Interview Questions on Logistic Regression

##### 1. What Are the Basic Assumption?

- Linear Relation between independent features and the log odds

##### 2. Advantages

Advantages of Logistics Regression

- Logistic Regression Are very easy to understand
- It requires less training
- Good accuracy for many simple data sets and it performs well when the dataset is linearly separable.
- It makes no assumptions about distributions of classes in feature space.
- Logistic regression is less inclined to over-fitting but it can overfit in high dimensional datasets.One may consider Regularization (L1 and L2) techniques to avoid over-fittingin these scenarios.
- Logistic regression is easier to implement, interpret, and very efficient to train.

##### 3. Disadvantages

- Sometimes Lot of Feature Engineering Is required
- If the independent features are correlated it may affect performance
- It is often quite prone to noise and overfitting
- If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting.
- Non-linear problems can’t be solved with logistic regression because it has a linear decision surface. Linearly separable data is rarely found in real-world scenarios.
- It is tough to obtain complex relationships using logistic regression. More powerful and compact algorithms such as Neural Networks can easily outperform this algorithm.
- In Linear Regression independent and dependent variables are related linearly. But Logistic Regression needs that independent variables are linearly related to the log odds (log(p/(1-p)).

##### 4. Whether Feature Scaling is required?

yes

#### 5. Missing Values

Sensitive to missing values

##### 6. Impact of outliers?

Like linear regression, estimates of the logistic regression are sensitive to the unusual observations: outliers, high leverage, and influential observations. Numerical examples and analysis are presented to demonstrate the most recent outlier diagnostic methods using data sets from medical domain

##### Types of Problems it can solve(Supervised)

- Classification

#### Practical Implementation

##### Performance Metrics

##### Classification

- Confusion Matrix
Precision,Recall, F1 score

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