Day: April 11, 2018

Support vector machine (SVM)

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The basis Support vector machine (SVM) is a supervised learning method. It can be used for regression or classification purposes. SVM is useful for hyper-text categorization, classification of images, recognition of characters… The basic visual idea is the creation of planes (lines) to separate features. The position of these planes can be adjusted to maximized the … Read more

Machine learning, source of errors

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Before to start What is an error? Observation prediction error = Target – Prediction = Bias + Variance + Noise The main sources of errors are Bias and Variability (variance). Underfitting or overfitting. Underclustering or overclustering. Improper validation (after the training). It could be that comes from the wrong validation set. It is important to divide … Read more

k-means clustering

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The basis K-means clustering is an unsupervised learning method. The aim is to find clusters and the CentroIDs that can potentially identify the What is a cluster? a set of data points grouped in the same category. What is a CentroID? center or average of a given cluster. What is “k”? the number of CentroIDs … Read more

Naive Bayes classification

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The basis It’s based on Bayes’ theorem (check the wikipedia link, and see how complex the decision trees could be). Assumes predictors contribute independently to the classification. Works well in supervised learning problems. Works with continuous and discrete data. Can work with discrete data sets. It is not sensitive to non-correlating “predictors”. Naives Bayes plot Example: … Read more

Understanding Logistics regression

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The basis Logistics or Logit regression. It’s a regression model where the dependent variable (DV) is categorical. Outcome follows a Bernoulli distribution. Success = 1 , failure = 0. Natural logarithm of odds ratio ln (p/1-p)… logit(p). Inverse log curve gives a nice “s” curve to work with. Equate logarithm of odds ratio with regression line equation. Solve for probability … Read more

Linear regression example

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I was looking for a simple example of a regression and how to calculate it by hand. I found this one: least squares example. the main formula to calculate the linear regression is   y = Ḇo + Ḇ1xcontinue learning the basis !