Logistic Regression
Regression analysis is a set of statistical methods used to estimate the relationships between a dependent variable and one or more independent variables. It can be used to assess the strength of the relationship between variables and to model the future relationship between them. There are many different regression models but linear and logistic regression is the most basic type of regression algorithm. The regression model is built upon the relationship between the dependent and independent variables in the dataset.
Introduction:
In this blog, we will discuss about the basic concepts of Logistic Regression. The logistic regression machine learning method is one of the simplest and simplest algorithms for sorting problems. Some examples of online trading fraud classification are malignant tumors and email spam classification. In logistic retrieval, the sigmoid logistic function is used to modify the output to return a probability value. Thus, the logistic return algorithm can be said to be a predictive analysis algorithm based on the concept of probability. Logistic retrieval is used to find the outcome of an event in binary values that may represent true or false. The result can be classified as 0 or 1, which represents failure or success, respectively.
Logistic Regression:
It is used for the classification problems, it can predictive analysis algorithm and based on the concept of probability.
logistic regression is statistical technique for analyzing a data set which can predicts the probability of an outcome that can only have two values.
Types of Logistic Regression:
- Binary logistic regression
Binary logistics regression is used to predict the potential of being values on independent variables.
- Multinomial logistic regression
It is used to guess the nominal dependent variable given one or more independent variables.
Advantages of Logistic Regression:
- The probability of an event occurring depending on the values of the independent variables, which can be categorical or numerical.
- It will estimate the probability that an event occurs for a randomly selected observation against the probability that the event does not occur.
- predict the effect of the series of variables on a binary response variable.
Limitations:
- Binary data does not have a normal distribution, which is a condition needed for most other types of regression.
- Predict values of dependent variable can be beyond 0 an 1,which violates the definition of probability.
- In logistic regression the predicted values has to be between 0 to 1.
Conclusion:
Logistic regression is a powerful tool, especially in epidemiological studies, allowing the simultaneous study of several interpretive variables, while minimizing the impact of confounders. However, researchers need to pay attention to the construction of the model, avoid feeding the software with only raw data and continue with the results. Some difficult decisions in construction models will depend entirely on the researcher’s experience in the field.
