Predictive capability is an important quality required by human beings. This ability has significant real-world applications too, specifically in the world of computers.
Machine learning algorithms can assist in the prediction of the likelihood of events in the point of view of historical data.
For instance, one can know whether it will rain today. Here comes the role of Exploring Logistic Regression. It is a predictive assessment technique that has its core on the probability concept.
Overview of logistic regression
Logistic regression is a supervised regression model used to predict a dependent categorical target variable.
Logistic regression can be useful if you want to categorize a huge set of data.
The key is that the dependent variable should be categorical as explained.
Logistic regression has set a standard in machine learning model. This model consists of a binary response variable (Y) which takes the value 0 or 1.
The variables of logistic regression can be classified into independent and dependent variables.
Independent variables can assume any value, and dependent variables depend on the independent variables.
What is a logistic function?
Logistic regression has got is name after the function used at its core: the sigmold function.
The sigmold function is also called as the logistic function. Being an S-shaped curve, the logistic function stretches from zero to one.
The logistic function is a special type of exponential function which generally models the augmented growth of a population.
Types of logistic regression
There are three primary types of logistic regression namely binary, multinomial and ordinal.
They are unique in their implementation and theory.
While binary regression works with two possible values, yes or no, multinomial regression works with two or more values.
Ordinal logistic regression works with three or more classes provided in a predetermined order.
Binary logistic regression
Binary logistic regression is an either/or solution. You can get only two possible outcomes.
This concept is generally shown as 0 or 1 in coding. Examples include will a team win in the game next week.
Multinomial logistic regression
In these models, the dependent variable is segregated into three or more categories, and these categories do not consist of any order. Examples include types of food.
Ordinal logistic regression
In these types of models, the dependent variable is classified into three or more categories, and these categories consist of an order. Example includes movie rating.
What is the application of logistic regression?
- In banks, Exploring Logistic Regression plays a crucial role in determining whether bank customers would miss paying their loans.This is instrumental in making out whether the bank will lend to the customer or not. Besides, it also helps in analyzing the maximum sum the bank will lend to those it has considered creditworthy.
- In medical research, logistic regression is used for calculating cancer risks. The researcher would assess the specific patient habits and certain other important factors as predictive elements.
- Logistic regression is famous in several natural language processing tasks. Email sorting is one good example of logistic regression where it shows good results.
- In the telecom industry, logistic regression will help the managers find out whether the customers who are using the service will be loyal to the company.
- A credit card company would wish to know whether the transaction amount and credit score influence the probability of a specific transaction being fraudulent. Here logistic regression can be performed.
Advantages of using logistic regression
- Logistic regression is comparatively easier to use for the purpose of machine learning.
- Logistic regression is a good solution in scenarios of the dataset being linearly separable.
- Logistic regression is helpful in measuring the relevance of an independent variable.
- The direction of the relation between the predicted parameters i.e. positive or negative is given in logistic regression.
- The algorithm in the scope of logistic regression lets models to be updated easily for the purpose of reflecting new data.
- Logistic regression produces well-measured probabilities.
- The outcome of a logistic regression is more insightful compared to other classification algorithms.
- With logistic regression you can generate reasonably useful predictions when you are faced with a complex linear problem and not a whole set of data.
Assumptions of logistic regression
Fundamental assumptions that must be adhered to for logistic regression comprise independence of errors, linearity in the logistic regression for continuous variables, dearth of multicollinearity, and lack of extreme outliers.
Logistic Regression vs. Linear Regression
Logistic regression and linear regression are the most commonly used ones.
The main difference between them is logistic regression is implemented when the dependent variable takes a binary form.
Besides, while logistic regression is applied when the target variable is categorical, linear regression is applied when dealing with continuous target variable.
Linear regression is implemented under instances of a continuous dependent variable and when the regression line takes a linear form.
Besides, linear regression expects a linear relationship between dependent and independent variables. This is not the case with logistic regression.
While you can implement linear regression testing to make out correlations between variables, independent variables share no correlations in logistic regression.
While linear regression makes use of positive and negative numbers to predict values and can come up with a range of values as result, logistic regressions provides only two results. The results are either one or zero.
While the key to logistic regression is precision, linear regression makes use of the root mean square error.
The estimation method used by logistic regression is either the least-square estimation method or the maximum likelihood estimation.
Linear regression applies the least-square estimation.
To be precise, compared to linear regression, logistic regression does not require the following:
- A linear relationship between the response variable and the explanatory variable
- The model residuals to be normally distributed
- The residuals to consist of homoscedasticity i.e. constant variance.
Take-home message
Since logistic functions produce the probability of occurrence of an event, it can be executed in several real-life situations.
This makes Exploring Logistic Regression a much sough-after regression model.
It is also a good thing for machine learning engineers and data scientists to have fair knowledge of logistic regression.
If you want to learn logistic regression, ensure to learn it from Softlogic!
