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Are there any alternatives to logistic regression in data science?
A wide range of alternatives are available, from statistics-based procedures (e.g. log binomial, ordinary or modified Poisson regression and Cox regression) to those rooted more deeply in data science such as machine learning and neural network theory.
Which is better LR or tree based regression?
Tree-based methods may outperform LR when it comes to classification, but they are more prone to overfitting than LR. This can be combated by “pruning” the tree. Another option is to try both LR and a decision tree to see which gives you the most desirable results.
Which is one can be used for both classification and regression?
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Are there any algorithms similar to random forest algorithm?
In my experiences so far, random forest overfit easily, SVM can generalize better, but it needs hyperparameter search to determinate the best learning parameters. all of these can be implemented in R software, by kernlab and e1071 packages for SVM, maxent and rFerns packages for 2 and 3 cases, respectively.
What should I do if my logistic regression is slow?
A recipe to get you a bit further is to just print (or log to a file) all the intermediate steps of your algorithm, the updated solution (I assume an iterative algorithm) and so on. This might just help pinpoint what is going wrong.
What’s the difference between logistic regression and tree based models?
Another important difference is how the models identify risk factors: logistic regression derives odds ratios for significant factors, while tree-based methods use tree-splitting (“ramifications”) to represent the risk factors; A probability of occurrence is assigned to each end of branch in the tree.
How is a logistic regression different from a linear regression?
Logistic regression (LR) models estimate the probability of a binary response, based on one or more predictor variables. Unlike linear regression models, the dependent variables are categorical. LR has become very popular, perhaps because of the wide availability of the procedure in software.