Fitting a machine learning model means finding optimal values for the parameters of the model. Sometimes this can be done in one step (when a closed form solution is available), but more often than not even if this can be done it is computationally prohibitively expensive. That is why most …
Cover art: Naive art by Ivan Generalic
Naive Bayes (NB) is 'naive' because it makes the assumption that features of a measurement are independent of each other. This is naive because it is (almost) never true. Here is why NB works anyway.
NB is a very intuitive classification algorithm. It …
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From November 2017 to January 2018 the Google Brain team hosted a speech recognition challenge on Kaggle. The goal of this challenge was to write a program that can correctly identify one of 10 words being spoken in a one-second long audio file. Having …
Logistic Regression is closely related to Linear Regression. Read my post on Linear Regression here
Logistic Regression is a classification technique, meaning that the target Y is qualitative instead of quantitative. For example, trying to predict whether a customer will stop doing business with you, a.k.a. churn.
Logistic …
Linear regression is used to model the relationship between continuous variables. For example to predict the price of a house when you have features like size in square meters and crime in the neighborhood etc. A linear regression function takes the form of
k-Nearest Neighbors or kNN is a classification algorithm that asigns a class to a new data point based on known data points that are similar, or nearby.
What do you mean 'nearby'?
To determine similarity of data you can use a few different distance algorithms. For example Euclidian distance, which …
Bayes Theorem
In statistics there are many situations where you want to determine the probability that a sample for which you have certain measurement belongs to a certain set. Say you want to know the chance that you have HIV if you test positive. No test is perfect, so this …