Logistic Regression
About Logistic Regression
In regression analysis, logistic regression (also known as logit regression) estimates a logistic model's parameters (the coefficients in the linear combination). Formally, binary logistic regression has a single binary dependent variable (two classes, coded by an indicator variable) with the values "0" and "1," whereas the independent variables can either be continuous variables or binary variables (two classes, coded by an indicator variable) (any real value).
The logistic function is the function that translates log odds to probability; therefore, the name. The associated probability of the value labeled "1" might range between 0 (surely the value "0") and 1 (definitely the value "1"); hence, the labeling.
Why is Logistic Regression important?
The technique of logistic regression has grown in significance in the field of machine learning. It enables machine learning algorithms to categorize incoming input based on previous data. The algorithms get more accurate at predicting classes within data sets when new pertinent data is added.
Who should take the Logistic Regression Exam?
- Working professionals who want to learn predictive modeling skills
- Graduates or undergraduates
Logistic Regression Certification Course Outline
- Logistic regression Modeling and applications
- Modeling Data
- Logistic regression Modeling Methodology
- Data Loading
- Variable Labeling
- Data QC
- Data Transformation
- Select Variables
- Deep Dive Analysis
- Transform Variables
- Develop Model
- Validate Model