Top 10 Data Mining Algorithms, Explained

By Raymond Li.

Here are the algorithms:

• 1. C4.5
• 2. k-means
• 3. Support vector machines
• 4. Apriori
• 5. EM
• 6. PageRank
• 7. AdaBoost
• 8. kNN
• 9. Naive Bayes
• 10. CART

1. C4.5

What does it do? C4.5 constructs a classifier in the form of a decision tree. In order to do this, C4.5 is given a set of data representing things that are already classified.

Wait, what’s a classifier? A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to.

What’s an example of this? Sure, suppose a dataset contains a bunch of patients. We know various things about each patient like age, pulse, blood pressure, VO₂max, family history, etc. These are called attributes.

Now:

Given these attributes, we want to predict whether the patient will get cancer. The patient can fall into 1 of 2 classes: will get cancer or won’t get cancer. C4.5 is told the class for each patient.

And here’s the deal:

Using a set of patient attributes and the patient’s corresponding class, C4.5 constructs a decision tree that can predict the class for new patients based on their attributes.

Cool, so what’s a decision tree? Decision tree learning creates something similar to a flowchart to classify new data. Using the same patient example, one particular path in the flowchart could be:

• Patient has a history of cancer
• Patient is expressing a gene highly correlated with cancer patients
• Patient has tumors
• Patient’s tumor size is greater than 5cm

The bottom line is:

The 10 Statistical Techniques Data Scientists Need to Master

By James Le, Freelance Full Stack Web Developer.

Regardless of where you stand on the matter of Data Science sexiness, it’s simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. Drawing on their vast stores of employment data and employee feedback, Glassdoor ranked Data Scientist #1 in their 25 Best Jobs in America list. So the role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers — and the companies that hire them — Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress.

While having a strong coding ability is important, data science isn’t all about software engineering (in fact, have a good familiarity with Python and you’re good to go). Data scientists live at the intersection of coding, statistics, and critical thinking. As Josh Wills put it, “data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.” I personally know too many software engineers looking to transition into data scientist and blindly utilizing machine learning frameworks such as TensorFlow or Apache Spark to their data without a thorough understanding of statistical theories behind them. So comes the study of statistical learning, a theoretical framework for machine learning drawing from the fields of statistics and functional analysis.

Why study Statistical Learning? It is important to understand the ideas behind the various techniques, in order to know how and when to use them. One has to understand the simpler methods first, in order to grasp the more sophisticated ones. It is important to accurately assess the performance of a method, to know how well or how badly it is working. Additionally, this is an exciting research area, having important applications in science, industry, and finance. Ultimately, statistical learning is a fundamental ingredient in the training of a modern data scientist. Examples of Statistical Learning problems include:

Top 10 Machine Learning Algorithms for Beginners

By Reena Shaw

I. Introduction

The study of ML algorithms has gained immense traction post the Harvard Business Review articleterming a ‘Data Scientist’ as the ‘Sexiest job of the 21st century’. So, for those starting out in the field of ML, we decided to do a reboot of our immensely popular Gold blog The 10 Algorithms Machine Learning Engineers need to know - albeit this post is targetted towards beginners.

ML algorithms are those that can learn from data and improve from experience, without human intervention. Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data; or ‘instance-based learning’, where a class label is produced for a new instance by comparing the new instance (row) to instances from the training data, which were stored in memory. ‘Instance-based learning’ does not create an abstraction from specific instances.

II. Types of ML algorithms

There are 3 types of ML algorithms:

1. Supervised learning:

Supervised learning can be explained as follows: use labeled training data to learn the mapping function from the input variables (X) to the output variable (Y).

Y = f (X)

Supervised learning problems can be of two types:

a. Classification: To predict the outcome of a given sample where the output variable is in the form of categories. Examples include labels such as male and female, sick and healthy.

b. Regression: To predict the outcome of a given sample where the output variable is in the form of real values. Examples include real-valued labels denoting the amount of rainfall, the height of a person.

The 1st 5 algorithms that we cover in this blog– Linear Regression, Logistic Regression, CART, Naïve Bayes, KNN are examples of supervised learning.

Ensembling is a type of supervised learning. It means combining the predictions of multiple different weak ML models to predict on a new sample. Algorithms 9-10 that we cover– Bagging with Random Forests, Boosting with XGBoost are examples of ensemble techniques.

How AI Learns What You’re Willing to Pay

By Ted Gaubert.

Why are we paying different prices? Is it ‘price personalization’ or a form of 'price discrimination'? The answer isn't so simple.

The world of Artificial Intelligence (AI) dynamic pricing engines is rapidly progressing and changing the competitive landscape. This article provides an overview of a few areas that influence how an AI pricing engine decides the price to show you:

1. Predicting market demand & micro-segmentation
2. Accumulating asymmetric information
3. Estimating ‘Willingness to Pay’
4. Shaping demand

1. Predicting Market Demand and Micro-Segmentation

AI micro-segmentation uses many customer attributes and behaviors to bucket customers by estimated willingness to pay. To explain in simple terms, let’s assume we have three buckets. We segment our customers by (A) high paying customers, (B) medium paying customers and (C) low paying customers. One strategy to maximize profits would be to first sell only to the high paying customer group A. Then any remaining seats could be sold to medium paying group B. Lastly, any leftovers could be sold to low paying group C.

Wait a second! We all know airline prices go up as the booking date gets closer to the departure date. Everyone knows to get a great price on an airline ticket you should book early!