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.,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.,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.,Second, the stumps that are created are not equally weighted in the final decision (final prediction). Stumps that create more error will have less say in the final decision.,AdaBoost is similar to Random Forests in the sense that the predictions are taken from many decision trees. However, there are three main differences that make AdaBoost unique:,For example, if we created one decision tree, the third one, it would predict 0. But if we relied on the mode of all 4 decision trees, the predicted value would be 1. This is the power of random forests!,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.,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.,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.,The time-blocking method’s fundamental vision is to know that you have something scheduled for every waking hour of your day. I ran into a few problems trying to apply this method within the modern scheduling platforms. Calendars were a mess, and before, I tried apps like Todoist and Things 3. But I still felt like the only concrete way to block out my time was to use an old stock calendar app. (gross),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.,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.,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.
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