Learning Types
There are mainly three type for machine learning types:
| Supervised Please teach me! | In supervised learning the agent observes some example input–output pairs and learns a function that maps from input to output. In component 1 above, the inputs are percepts and the output are provided by a teacher who says “Brake!” or “Turn left.” In component 2, the inputs are camera images and the outputs again come from a teacher who says “that’s a bus.” In 3, the theory of braking is a function from states and braking actions to stopping distance in feet. In this case the output value is available directly from the agent’s percepts (after the fact); the environment is the teacher. |
| Unsupervised I can learn myself! | In unsupervised learning the agent learns patterns in the input even though no explicit feedback is supplied. The most common unsupervised learning task is clustering: detecting potentially useful clusters of input examples. For example, a taxi agent might gradually develop a concept of “good traffic days” and “bad traffic days” without ever being given labeled examples of each by a teacher. |
| Reinforcement My way or highway! | In reinforcement learning the agent learns from a series of reinforcements—rewards or punishments. For example, the lack of a tip at the end of the journey gives the taxi agent an indication that it did something wrong. The two points for a win at the end of a chess game tells the agent it did something right. It is up to the agent to decide which of the actions prior to the reinforcement were most responsible for it. |
| Semi-supervised | In semi-supervised learning we are given a few labeled examples and must make what we can of a large collection of unlabeled examples. Even the labels themselves may not be the oracular truths that we hope for. Imagine that you are trying to build a system to guess a person’s age from a photo. You gather some labeled examples by snapping pictures of people and asking their age. That’s supervised learning. But in reality some of the people lied about their age. It’s not just that there is random noise in the data; rather the inaccuracies are systematic, and to uncover them is an unsupervised learning problem involving images, self-reported ages, and true (unknown) ages. Thus, both noise and lack of labels create a continuum between supervised and unsupervised learning. |
Prediction Target
Prediction target (also called as target variable or dependent variable) is outcome of the machine learning process. The type of prediction target determines the selection of algorithms and processes for prediction. There are mainly two types of outcome as follows:
| Qualitative | Qualitative (Categorical) – non-numerical Binary – strictly 2 possible values (e.g.: true/false) Multinomial – more than 2 values |
| Quantitative | Quantitative (Numeric) Continuous – can take any numerical value and are measured Count – variables are numeric, non-negative, and result from counting rather than ranking |
Process
Process is a type of learning problems that a machine learning system tries to solve. These processes are selected based on learning types and required type of prediction target.
| Classification | A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”. |
| Regression | A regression problem is when the output variable is a real value, such as “Salary” or “Price”. |
| Clustering | A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior. |
| Association | An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y. |