Contents
How to improve the performance of a predictive model?
Depending on the performance of your current model and whether it is suffering more from high bias or high variance, you can resort to one or more of these seven techniques to bring your model where you want it to be: Add More Data! Add More Features! Do Feature Selection. Use Regularization. Bagging is short for Bootstrap Aggregation.
How does feature selection improve accuracy and training time?
Reduces Overfitting: Less redundant data means less opportunity to make decisions based on noise. Improves Accuracy: Less misleading data means modeling accuracy improves. Reduces Training Time: Less data means that algorithms train faster. Weka provides an attribute selection tool. The process is separated into two parts:
How can I reduce a set of features?
These new reduced set of features should then be able to summarize most of the information contained in the original set of features. In this way, a summarised version of the original features can be created from a combination of the original set.
Which is the best way to regularize a model?
Regularization relies on smart implementations of training algorithms and is usually the much preferred version of feature selection. Bagging is short for Bootstrap Aggregation. It uses several versions of the same model trained on slightly different samples of the training data to reduce variance without any noticeable effect on bias.
Which is the War Horse of predictive modeling?
This is a figure I dug up from an old slide deck I prepared years ago for a workshop on predictive modeling. it illustrates what I think of as the “war horse” of model tuning (‘cause you know, it kind of looks like a horse, with an extra spear). It also is a kind of map for navigating the Bias-Variance space.
What does bias mean in a predictive model?
Bias in predictive models is a measure of model rigidity and inflexibility, and means that your model is not capturing all the signal it could from the data. Bias is also known as under-fitting.
Which is a component of imprecision in predictive models?
Bias and variance are the two components of imprecision in predictive models, and in general there is a trade-off between them, so normally reducing one tends to increase the other.