In case it doesn’t make sense yet, consider the image above. Comparing the green line of best fit to the red line, notic

Ensemble learning is a method where multiple learning algorithms are used in conjunction. The purpose of doing so is that it allows you to achieve higher predictive performance than if you were to use an individual algorithm by itself.

In mathematical terms, you can write this as the probability of it being hot GIVEN that you played golf. The mathematical notation is P(hot|yes). This is known as conditional probability and is essential to understand the rest of what I’m about to say.

Bootstrap sampling is a resampling method that uses random sampling with replacement. It sounds complicated but trust me when I say it’s REALLY simple — read more about it here.

K-nearest neighbors is a simple idea. First, you start off with data that is already classified (i.e. the red and blue data points). Then when you add a new data point, you classify it by looking at the k nearest classified points. Whichever class gets the most votes determines what the new point gets classified as.

Let’s assume that there are two classes of data. A support vector machine will find a hyperplane or a boundary between the two classes of data that maximizes the margin between the two classes (see above). There are many planes that can separate the two classes, but only one plane can maximize the margin or distance between the classes.

To find the weights of the initial equation to calculate the score, methods like gradient descent or maximum likelihood are used. Since it’s beyond the scope of this article, I won’t go into much more detail, but now you know how it works!

Logistic regression is similar to linear regression but is used to model the probability of a discrete number of outcomes, typically two. At a glance, logistic regression sounds much more complicated than linear regression, but really only has one extra step.

The extra step is feeding the score that you previously calculated in the sigmoid function below so that you get a probability in return. This probability can then be converted to a binary output, either 1 or 0.

Bagging when you use the aggregate of the bootstrapped datasets to make a decision — I dedicated an article to this topic so feel free to check it out here if this doesn’t make complete sense.

Random forests are an ensemble learning technique that builds off of decision trees. Random forests involve creating multiple decision trees using bootstrapped datasets of the original data and randomly selecting a subset of variables at each step of the decision tree. The model then selects the mode of all of the predictions of each decision tree (bagging). What’s the point of this? By relying on a “majority wins” model, it reduces the risk of error from an individual tree.

Something to keep in mind is that if the value of k is set too low, it can be subject to outliers. On the other hand, if the value of k is set too high then it might overlook classes with only a few samples.

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lder than her, going away for college divided us for a little while, but we quickly rekindled our relationship. Once we were both in college, we went our separate ways, studied different things, and had different interests. And at one point, those interests clashed.

A Support Vector Machine is a supervised classification technique that can actually get pretty complicated but is pretty intuitive at the most fundamental level. For the sake of this article, we’ll keep it pretty high level.

Naive Bayes can seem like a daunting algorithm because it requires preliminary mathematical knowledge in conditional probability and Bayes Theorem, but it’s an extremely simple and ‘naive’ concept, which I’ll do my best to explain with an example:

Since P(yes|X) > P(no|X), then you can predict that this person would play golf given that the outlook is sunny, the temperature is mild, the humidity is normal and it’s not windy.