Stationary Point Definition of stationary point from wikipedia : In mathematics, particularly in calculus, a stationary point or critical point of a differentiable function of one variable is a point on the graph of the function where the function's derivative is zero. Informally, it is a point where the function "stops" increasing or decreasing… Continue reading Lagrange Multiplier With Equality Constraints
In the blog post on Cost Function And Hypothesis for LR we noted that LR (Logistic Regression) inherently models binary classification. Here we will describe two approaches used to extend it for multiclass classification. One vs Rest approach takes one class as positive and rest all as negative and trains the classifier. So for the data having… Continue reading Classification – One vs Rest and One vs One
Some Scenarios In finance default is failure to meet the legal obligation of loan. Given some data we want to classify whether the person will be defaulter or not. Suppose our training data-set is imbalanced. Out of 10k samples only 300 are defaulters. (3%) Classifier in the following table is good at classifying non defaulters… Continue reading On Classification Accuracy
Andrew N.G talks about plotting learning curves to check if your model is suffering from high bias or a variance problem. So I tried to plot this curve. One thing I noticed that this curves were very noisy hence I have applied moving average to it. My data has around 1450 examples and I am… Continue reading Learning Curves
When should we split data in three parts (namely train, cv, test) or just two parts (train and test)? Ref : http://stats.stackexchange.com/questions/19048/what-is-the-difference-between-test-set-and-validation-set When you apply the model to real world data, you want a number which says how much confident you are, say 80%. What if you divide data in two parts? Train, Test In case… Continue reading Cross-validation in sklearn
So sometimes we get many many feature variable, say 50 for now. (Can be much more in practice). And model is always good with small features unless they are necessary. Technically it is knows as dimensionality reduction and keeps model from over-fitting. Here is the great blog about this. It talks about correlation and mutual information.… Continue reading Dimentionality Reduciton – Correlation
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