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Results tagged with machine-learning
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user 344849
Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.
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How explainable is Linear Discriminant Analysis?
In a survey paper on the interpretability of various machine learning algorithms, I didn't find Linear Discriminant Analysis (LDA). I wonder how explainable is LDA to audiences not familiar with mach …
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Accepted
How to compare two random forests in scikit-learn?
In logistic regression, the coefficients of a variable indicates the expected change in log odds of observed outcome, per unit change in that variable. So by looking at these model parameters, one can …
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Feature selection- feature that most of its values are equal. How can I know if to drop it?
Is there a reason why you would select the features in advance? Otherwise, most likely during the modeling process, you will obtain insight whether to include or drop this feature.
For example, if you …
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Exploratory data analysis - Feature influence on outcome
With a large penalty, regularization method lasso (L1) can help to push the coefficients of some variables to 0, so it works as if you had done feature selection. However, ridge (L2) only pushes the c …
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Range of Values for Hyperparameter Fine-Tuning in Random Forest Classification
you are right that the random forest or other tree ensemble methods make it hard for overfitting. Essentially, you can set the number of trees to be very large, it is uncommon to have 5000 trees. The …
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When interpreting machine learning models, should preprocessing steps be considered as part ...
Depending on what exactly the preprocessing steps are. If you have applied PCA and then feed the principal components as features to the random forest model, then by design you have greatly reduced th …
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