Logistic Regression vs Decision Trees vs SVM: Part II

This is the 2nd part of the series. Read the first part here: Logistic Regression Vs Decision Trees Vs SVM: Part I

In this part we’ll discuss how to choose between Logistic Regression , Decision Trees and Support Vector Machines. The most correct answer as mentioned in the first part of this 2 part article , still remains it depends. We’ll continue our effort to shed some light on, it depends on what. All three of these techniques have certain properties inherent by their design, we’ll elaborate on some in order to provide you with few pointers on their selection for your particular business problem.

We’ll start with Logistic Regression , the most prevalent algorithm for solving industry scale problems, although its losing ground to other techniques with progress in efficiency and implementation ease of other complex algorithms.

A very convenient and useful side effect of a logistic regression solution is that it doesn’t give you discrete output or outright classes as output. Instead you get probabilities associated with each observation. You can apply many standard and custom performance metrics on this probability score to get a cutoff and in turn classify output in a way which best fits your business problem. A very popular application of this property is scorecards in the financial industry ,where you can adjust your threshold [cutoff ] to get different results for classification from the same model. Very few other algorithms provide such scores as a direct result. Instead their outputs are discreet direct classifications. Also, logistic regression is pretty efficient in terms of time and memory requirement. It can be applied on distributed data and it also has online algorithm implementation to handle large data on less resources.

In addition to above, logistic regression algorithm is robust to small noise in the data and is not particularly affected by mild cases of multi-collinearity. Severe cases of multi-collinearity can be handled by implementing logistic regression with L2 regularization, although if a parsimonious model is needed, L2 regularization is not the best choice because it keeps all the features in the model.

Where logistic regression starts to falter is , when you have a large number of features and good chunk of missing data. Too many categorical variables are also a problem for logistic regression. Another criticism of logistic regression can be that it uses the entire data for coming up with its scores. Although this is not a problem as such , but it can be argued that “obvious” cases which lie at the extreme end of scores should not really be a concern when you are trying to come up with a separation curve. It should ideally be dependent on those boundary cases, some might argue. Also if some of the features are non-linear, you’ll have to rely on transformations, which become a hassle as size of your feature space increases. We have picked few prominent pros and cons from our discussion to summaries things for logistic regression.

Logistic Regression Pros:

  • Convenient probability scores for observations
  • Efficient implementations available across tools
  • Multi-collinearity is not really an issue and can be countered with L2 regularization to an extent
  • Wide spread industry comfort for logistic regression solutions [ oh that’s important too!]

Logistic Regression Cons:

  • Doesn’t perform well when feature space is too large
  • Doesn’t handle large number of categorical features/variables well
  • Relies on transformations for non-linear features
  • Relies on entire data [ Not a very serious drawback I’d say]

Read the complete post at http://www.edvancer.in/logistic-regression-vs-decision-trees-vs-svm...

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Tags: analytics, data, logistic, predictive, regression, science, svm


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