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Category: Data Science

Basics Of Survival Analysis

Published 2017-09-14 by Kevin Feasel

Subhasree Chatterjee explains the basics of survival analysis:

Survival analysis is a set of methods to analyze the ‘time to occurrence’ of an event. The response is often referred to as a failure time, survival time, or event time. These methods are widely used in clinical experiments to analyze the ‘time to death’, but nowadays these methods are being used to predict the ‘when’ and ‘why’ of customer churn or employee turnover as well.

The dependent variables for the analysis are generally two functions:

Read the whole thing.  H/T R-Bloggers

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Understanding Neural Networks: Perceptrons

Published 2017-09-13 by Kevin Feasel

Akash Sethi explains what a perceptron is:

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
Linear classifier defined that the training data should be classified into corresponding categories i.e. if we are applying classification for the 2 categories then all the training data must be lie in these two categories.
Binary classifier defines that there should be only 2 categories for classification.
Hence, The basic Perceptron algorithm is used for binary classification and all the training example should lie in these categories. The basic unit in the Neuron is called the Perceptron.

Click through to learn more about perceptrons.

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Uses Of kd-trees

Published 2017-09-13 by Kevin Feasel

Sandipan Dey explains what a kd-tree is and how it works:

The prime advantage of a 2d-tree over a BST is that it supports efficient implementation of range search and nearest-neighbor search. Each node corresponds to an axis-aligned rectangle, which encloses all of the points in its subtree. The root corresponds to the entire plane [(−∞, −∞), (+∞, +∞ )]; the left and right children of the root correspond to the two rectangles split by the x-coordinate of the point at the root; and so forth.

  • Range search: To find all points contained in a given query rectangle, start at the root and recursively search for points in both subtrees using the following pruning rule: if the query rectangle does not intersect the rectangle corresponding to a node, there is no need to explore that node (or its subtrees). That is, search a subtree only if it might contain a point contained in the query rectangle.

  • Nearest-neighbor search: To find a closest point to a given query point, start at the root and recursively search in both subtrees using the following pruning rule: if the closest point discovered so far is closer than the distance between the query point and the rectangle corresponding to a node, there is no need to explore that node (or its subtrees). That is, search a node only if it might contain a point that is closer than the best one found so far. The effectiveness of the pruning rule depends on quickly finding a nearby point. To do this, organize the recursive method so that when there are two possible subtrees to go down, you choose first the subtree that is on the same side of the splitting line as the query point; the closest point found while exploring the first subtree may enable pruning of the second subtree.

  • k-nearest neighbors search: This method returns the k points that are closest to the query point (in any order); return all n points in the data structure if n ≤ k. It must do this in an efficient manner, i.e. using the technique from kd-tree nearest neighbor search, not from brute force.

Sandipan implements a fairly classic problem in this space:  the behavior of a group of flocking birds.

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Naive PCA With R

Published 2017-09-12 by Kevin Feasel

Pablo Bernabeu gives us a naive method for performing a Principal Component Analysis:

STAGE 1.  Determine whether PCA is appropriate at all, considering the variables

  • Variables should be inter-correlated enough but not too much. Field et al. (2012) provide some thresholds, suggesting that no variable should have many correlations below .30, or any correlation at all above .90. Thus, in the example here, variable Q06 should probably be excluded from the PCA.

  • Bartlett’s test, on the nature of the intercorrelations, should be significant. Significance suggests that the variables are not an ‘identity matrix’ in which correlations are a sampling error.

  • KMO (Kaiser-Meyer-Olkin), a measure of sampling adequacy based on common variance (so similar purpose as Bartlett’s). As Field et al. review, ‘values between .5 and .7 are mediocre, values between .7 and .8 are good, values between .8 and .9 are great and values above .9 are superb’ (p. 761). There’s a general score as well as one per variable. The general one will often be good, whereas the individual scores may more likely fail. Any variable with a score below .5 should probably be removed, and the test should be run again.

  • Determinant: A formula about multicollinearity. The result should preferably fall below .00001.

PCA is a powerful tool in several fields, including clinical testing.

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The Use And Misuse Of P Values

Published 2017-09-11 by Kevin Feasel

John Mount and Nina Zumel explain what p-values are and how people routinely misuse them:

The many things I happen to have issues with in common mis-use of p-values include:

  1. p-hacking. This includes censored data bias, repeated measurement bias, and even outright fraud.

  2. “Statsmanship” (the deliberate use of statistical terminology for obscurity, not for clarity). For example: saying p instead of saying what you are testing such as “significance of a null hypothesis”.

  3. Logical fallacies. This is the (false) claim that p being low implies that the probability that your model is good is high. At best a low-p eliminates a null hypothesis (or even a family of them). But saying such disproof “proves something” is just saying “the butler did it” because you find the cook innocent (a simple case of a fallacy of an excluded middle).

  4. Confusion of population and individual statistics. This is the use of deviation of sample means (which typically decreases as sample size goes up) when deviation of individual differences (which typically does not decrease as sample size goes up) is what is appropriate . This is one of the biggest scams in data science and marketing science: showing that you are good at predicting aggregate (say, the mean number of traffic deaths in the next week in a large city) and claiming this means your model is good at predicting per-individual risk. Some of this comes from the usual statistical word games: saying “standard error” (instead of “standard error of the mean or population”) and “standard deviation” (“instead of standard deviation of individual cases”); with some luck somebody won’t remember which is which and be too afraid to ask.

Even if you know what p-values are, this is definitely worth reading, as it’s so easy to misuse p-values (even when I’m not on my Bayesian post hurling tomatoes at frequentists).

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Overfitting On Decision Trees

Published 2017-09-08 by Kevin Feasel

Ramandeep Kaur explains overfitting as well as how to prevent overfitting on decision trees:

Causes of Overfitting

There are two major situations that could cause overfitting in DTrees:

  1. Overfitting Due to Presence of Noise – Mislabeled instances may contradict the class labels of other similar records.
  2. Overfitting Due to Lack of Representative Instances – Lack of representative instances in the training data can prevent refinement of the learning algorithm.

                      A good model must not only fit the training data well
                      but also accurately classify records it has never seen.

How to avoid overfitting?

There are 2 major approaches to avoid overfitting in DTrees.

  1. approaches that stop growing the tree earlier, before it reaches the point where it perfectly classifies the training data.

  2. approaches that allow the tree to overfit the data, and then post-prune the tree.

Click through for more details on these two approaches.

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Learning Naive Bayes

Published 2017-09-05 by Kevin Feasel

Sunil Ray explains the Naive Bayes algorithm:

What are the Pros and Cons of Naive Bayes?

Pros:

  • It is easy and fast to predict class of test data set. It also perform well in multi class prediction
  • When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.
  • It perform well in case of categorical input variables compared to numerical variable(s). For numerical variable, normal distribution is assumed (bell curve, which is a strong assumption).

Cons:

  • If categorical variable has a category (in test data set), which was not observed in training data set, then model will assign a 0 (zero) probability and will be unable to make a prediction. This is often known as “Zero Frequency”. To solve this, we can use the smoothing technique. One of the simplest smoothing techniques is called Laplace estimation.

  • On the other side naive Bayes is also known as a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.

  • Another limitation of Naive Bayes is the assumption of independent predictors. In real life, it is almost impossible that we get a set of predictors which are completely independent.

Read the whole thing.  Naive Bayes is such an easy algorithm, yet it works remarkably well for categorization problems.  It’s typically not the best solution, but it’s a great first solution.  H/T Data Science Central

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Text Featurizing With Microsoft R Server

Published 2017-09-01 by Kevin Feasel

David Smith has a post summarizing sentiment analysis with Microsoft R Server:

Tsuyoshi Matsuzaki demonstrates the process in a post at the MSDN Blog. The post explores the Multi-Domain Sentiment Dataset, a collection of product reviews from Amazon.com. The dataset includes reviews from 975,194 products on Amazon.com from a variety of domains, and for each product there is a text review and a star rating of 1, 2, 4, or 5. (There are no 3-star rated reviews in the data set.) Here’s one example, selected at random:

What a useful reference! I bought this book hoping to brush up on my French after a few years of absence, and found it to be indispensable. It’s great for quickly looking up grammatical rules and structures as well as vocabulary-building using the helpful vocabulary lists throughout the book. My personal favorite feature of this text is Part V, Idiomatic Usage. This section contains extensive lists of idioms, grouped by their root nouns or verbs. Memorizing one or two of these a day will do wonders for your confidence in French. This book is highly recommended either as a standalone text, or, preferably, as a supplement to a more traditional textbook. In either case, it will serve you well in your continuing education in the French language.

The review contains many positive terms (“useful”, “indespensable”, “highly recommended”), and in fact is associated with a 5-star rating for this book. The goal of the blog post was to find the terms most associated with positive (or negative) reviews. One way to do this is to use the featurizeText function in thje Microsoft ML package included with Microsoft R Client and Microsoft R Server. Among other things, this function can be used to extract ngrams (sequences of one, two, or more words) from arbitrary text. In this example, we extract all of the one and two-word sequences represented at least 500 times in the reviews. Then, to assess which have the most impact on ratings, we use their presence or absence as predictors in a linear model:

If you’re thinking about sentiment analysis, read the whole thing.

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R Versus Python

Published 2017-08-31 by Kevin Feasel

Vincent Granville believes that Python is overtaking R in the realm of data science:

We use the app in question to compare search interest for R data Science versus Python Data Science, see above chart.  It looks like until December 2016, R dominated, but fell below Python by early 2017. The above chart displays an interest index, 100 being maximum and 0 being minimum. Click here to access this interactive chart on Google, and check the results for countries other than US, or even for specific regions such as California or New York.

Note that Python always dominated R by a long shot, because it is a general-purpose language, while R is a specialized language. But here, we compare R and Python in the niche context of data science. The map below shows interest for Python (general purpose) per region, using the same Google index in question.

It’s an interesting look at the relative shift between R and Python as a primary language for statistical analysis.

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Tokenizing Text With R

Published 2017-08-30 by Kevin Feasel

Rachael Tatman shows how to tokenize a set of text as the first step in a natural language processing experiment:

In this tutorial you’ll learn how to:

  • Read text into R
  • Select only certain lines
  • Tokenize text using the tidytext package
  • Calculate token frequency (how often each token shows up in the dataset)
  • Write reusable functions to do all of the above and make your work reproducible

For this tutorial we’ll be using a corpus of transcribed speech from bilingual children speaking in English.  You can find more information on this dataset and download it here.

It’s a nice tutorial, especially because the data set is a bit of a mess.

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