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Logistic Regression Advantages

Logistic Regression is a useful classification algorithm that’s capable of high performance, high accuracy and probability results. Also, it comes with cool tuning options and lots of regularization parameters to address overfitting risk.

We compiled a list of Logistic Regression Pros below.

1- Good Performance

Logistic Regression has very favorable runtime performance and it scales well. This characteristic makes it a suitable machine learning algorithm for big data problems.

Also, Logistic Regression is suitable for parallel computation. This will make it run even faster with multicore processors and it is quite easy to optimize it to run with parallelization. You can see a tutorial below:

Tuning Logistic Regression

2- Regularization

You can do lots of optimization tricks with Logistic Regression models and this offers increased control over the way the model behaves.

To give some examples to the regularization related hyperparameters we have C for regularization penalty strength, penalty for regularization criteria, solver for solver to use with regularization. Regularization can be useful to control and avoid overfitting issues.

3- Probabilistic Reports

There aren’t many machine learning algorithms that come with probability reports and this is a very welcome feature for some applications.

Sometimes you may want to see probability of each prediction in addition to the prediction itself. This may be helpful when prediction is used to trigger another function or when the model is used as an input to another machine learning algorithm in a hybrid setup.

No assumptions

4- Assumption free prediction

If you have visited our Naive Bayes Tutorials you may remember that Naive Bayes makes assumptions regarding features in the dataset. That’s how it gets the name naive.

Logistic Regression is free of assumptions. It doesn’t make any assumptions about the dataset or its features and this means you don’t have to worry about specific characteristics such as independence of variables in the dataset too much.

Accurate for linear data

5- Highly accurate

When data is suitable for Logistic Regression, it is a highly accurate machine learning algorithm. For Logistic Regression to perform accurately data needs to have linear relationship. Logistic Regression won’t perform very well with non-linear or arbitrary relationships.

Useful classifier with probability reports


Logistic Regression is a statistical technique that’s computationally efficient, scales well, predicts well when suitable and produces probability values for predictions.

It probably owes to these advantages still being so popular when there is a strong competition. If you are interested in implementing Logistic Regression you might want to also know some of its disadvantages. You can see a tutorial we have about that below.

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