If correlation doesn’t imply causation, then what does? As a data scientist, it is often quite frustrating to work with correlation and not be able to draw conclusive causality. The best way to confidently obtain causality is, usually, through randomized experiments, such as the ones we saw in Chapter 8, Advanced Statistics. One would have […]
On the more formal side of things – Communicating Data
On the more formal side of things When presenting data findings to a more formal audience, I like to stick to the following six steps: By following these steps, we can hit on all of the major areas of the data science method. The first thing you want to hit on during a formal presentation […]
Introducing ML – How to Tell if Your Toaster is Learning – Machine Learning Essentials
Introducing ML In Chapter 1, Data Science Terminology, we defined ML as giving computers the ability to learn from data without being given explicit rules by a programmer. This definition still holds true. ML is concerned with the ability to ascertain certain patterns (signals) out of data, even if the data has inherent errors in […]
Example – facial recognition – How to Tell if Your Toaster is Learning – Machine Learning Essentials
Example – facial recognition This problem is, on its face (pun intended), quite simple: given a picture of a face, who does it belong to? However, let’s consider a slightly simpler task. Suppose you wish to implement a home security system that recognizes who is entering your house. Most likely, during the day, your house […]
ML isn’t perfect – How to Tell if Your Toaster is Learning – Machine Learning Essentials
ML isn’t perfect There are many caveats of ML. Many are specific to different models being implemented, but some assumptions are universal for any ML model: This assumption is particularly important. Many ML models take this assumption very seriously. These models are not able to communicate that there might not be a relationship. These assumptions […]
How does ML work? – How to Tell if Your Toaster is Learning – Machine Learning Essentials
How does ML work? Each flavor of ML and each individual model works in very different ways, exploiting different parts of mathematics and data science. However, in general, ML works by taking in data, finding relationships within the data, and giving as output what the model learned, as illustrated in Figure 10.2. Figure 10.2 – […]
Example – heart attack prediction – How to Tell if Your Toaster is Learning – Machine Learning Essentials
Example – heart attack prediction Suppose we wish to predict whether someone will have a heart attack within a year. To predict this, we are given that person’s cholesterol level, blood pressure, height, smoking habits, and perhaps more. From this data, we must ascertain the likelihood of a heart attack. Suppose, to make this prediction, […]
Types of SL models – How to Tell if Your Toaster is Learning – Machine Learning Essentials
Types of SL models There are, in general, two types of SL models: regression and classification models. The difference between the two is quite simple and lies in the nature of the response variable. Regression models attempt to predict a continuous response. This means that the response can take on a range of infinite values. […]
UL – How to Tell if Your Toaster is Learning – Machine Learning Essentials
UL The second type of ML on our list does not deal with making predictions but has a much more open objective. UL takes in a set of predictors and utilizes relationships between the predictors in order to accomplish tasks such as the following: Both of these are examples of UL because they do not […]
RL – How to Tell if Your Toaster is Learning – Machine Learning Essentials
RL In RL, algorithms, referred to as agents, learn to make decisions by interacting with an environment. The agent selects an action to take based on its current state and then receives a reward or penalty based on the outcome of that action. The goal is to learn a policy—a mapping from states to actions—that […]