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definition of ml

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a machine is said to learn if it is able to take experience and utilize it such that its performance improves up on similar experiences

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knowledge representation

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"The formation of logical structures that assist with turning raw sensory information into a meaningful insight.

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During the process of knowledge representation

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the computer summarizes raw inputs in a model, an explicit description of the structured patterns among data. There are many different types of models.

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You may already be familiar with some. Examples include:

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...

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Equations

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...

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Diagrams such as trees and graphs

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...

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Logical if/else rules

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...

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Groupings of data known as clusters"

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...

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training

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the process of fitting a particular model to a dataset

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learning vs. training

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"Learning requires an additional step to generalize the knowledge to future data. Second, the term training more accurately describes the actual process undertaken when a model is fitted to the data.

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Learning implies a sort of inductive

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bottom-up reasoning. Training better connotes the fact that the machine learning model is imposed by the human teacher onto the machine student, providing the computer with a structure it attempts to model after."

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generalization

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The term generalization describes the process of turning abstracted knowledge into a form that can be utilized for action.

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heuristics

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"machine learning algorithms generally employ shortcuts that more quickly divide the set

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of concepts. Toward this end

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the algorithm will employ heuristics, or educated guesses about where to find the most important concepts."

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bias

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"The heuristics employed by machine learning algorithms also sometimes result in erroneous conclusions. If the conclusions are systematically imprecise, the algorithm is said

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to have a bias."

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...

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noise

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"the failure for models to perfectly generalize is due to the problem of noise, or unexplained variations in data."

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overfitting

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"Trying to model the noise in data is the basis of a problem called overfitting. Because noise is unexplainable by definition, attempting to explain the noise will result in erroneous conclusions that do not generalize well to new cases. Attempting to generate theories to explain the noise also results in more complex models that are more likely to ignore the true pattern the learner is trying to identify. A model that seems to perform well during training but does poorly during testing is said to be overfitted to the training dataset as it does not generalize well. "

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steps to apply machine learning to your data

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"collecting data

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exploring and preparing data

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...

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training a model on the data

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...

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evaluating model performance

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...

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improving model performance"

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...

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____ percent of the effort in machine learning is devoted to data.

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80 (often cited statistic)

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example

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An example is literally a single exemplary instance of the underlying concept to be learned; it is one set of data describing the atomic unit of interest for the analysis.

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unit of observation

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The phrase unit of observation is used to describe the units that the examples are measured in.

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feature

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"A feature is a characteristic or attribute of an example, which might be useful for learning the desired concept."

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"______ format data is by far the most common form used in machine learning

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though as you will see in later chapters, other forms are used occasionally in specialized cases.",Matrix

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"numeric

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categorical, ordinal","If a feature represents a characteristic measured in numbers, it is unsurprisingly called numeric. Alternatively, if it measures an attribute that is represented by a set of categories, the feature is called categorical or nominal. A special case of categorical variables is called ordinal, which designates a nominal variable with categories falling in an ordered list."

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predictive model

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"A predictive model is used for tasks that involve, as the name implies, the prediction of one value using other values in the dataset. The learning algorithm attempts to discover and model the relationship among the target feature (the feature being predicted) and the other features. Despite the common use of the word ""prediction"" to imply forecasting predictive models need not necessarily foresee future events. For instance, a predictive model could be used to predict past events such as the date of a baby's conception using the mother's hormone levels; or, predictive models could be used in real time to control traffic lights during rush hours."

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supervised learning

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the process of training a predictive model is known as supervised learning.

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classification

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The often-used supervised machine learning task of predicting which category an example belongs to is known as classification

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"class

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levels","The target feature to be predicted is a categorical feature known as the class and is divided into categories called levels. A class can have two or more levels, and the levels need not necessarily be ordinal. Because classification is so widely used in machine learning, there are many types of classification algorithms."

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numeric prediction

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"To predict such numeric values, a common form of numeric prediction fits linear regression models to the input data. Although regression models are not the only type of numeric models, they are by far the most widely used. Regression methods are widely used for forecasting, as they quantify in exact terms the association between the inputs and the target, including both the magnitude and uncertainty of the relationship."

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unsupervised learning

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"because there is no target to learn, the process of training a descriptive model is called unsupervised learning."

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descriptive model

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"A descriptive model is used for tasks that would benefit from the insight gained from summarizing data in new and interesting ways. As opposed to predictive models that predict a target of interest; in a descriptive model, no single feature is more important than any other. In fact, because there is no target to learn, the process of training a descriptive model is called unsupervised learning. "

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pattern discovery

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the descriptive modeling task called pattern discovery is used to identify frequent associations within data. Pattern discovery is often used for market basket analysis on transactional purchase data.

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clustering

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The descriptive modeling task of dividing a dataset into homogeneous groups is called clustering.

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four types of tasks to match a learning task to a ml approach

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"classification, numeric prediction, pattern detection, clustering"

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"for _____

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more thought is needed to match a learning problem to an appropriate classifier",

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supervised learing algorithms

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"Nearest Neighbor

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naive Bayes

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...

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Decision Trees

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...

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Classification Rule Learners

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...

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Linear Regression

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...

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Regression Trees

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...

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Model Trees

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...

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Neural Networks

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...

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Support Vector Machines"

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...

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unsupervised learning algorithms

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"Association Rules

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k-means Clustering"

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...

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algo: Nearest Neighbor

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supervised; classification

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algo: naive Bayes

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"supervised, classification"

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algo: Decision Trees

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"supervised, classification"

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algo: Classification Rule Learners

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"supervised, classification"

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algo: Linear Regression

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"supervised, numeric prediction"

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algo: Regression Trees

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"supervised, numeric prediction"

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algo: Model Trees

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"supervised, numeric prediction"

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algo: Neural Networks

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"supervised, dual use"

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algo: Support Vector Machines

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"supervised, dual use"

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algo: Association Rules

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"unsupervised, pattern detection"

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algo: k-means clustering

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"unsupervised, Clustering"

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(supervised) numeric prediction algos:

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"Linear Regression, Regression Trees, Model Trees"

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(supervised) dual use algos:

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"Neural Networks

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Support Vector Machines"

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...

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(supervised) single use classification algos:

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"Nearest Neighbor

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naive Bayes

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...

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Decision Trees

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...

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Classification Rule Learners"

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...

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Pattern detection algo:

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Association Rules

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Clustering algo:

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k-means clustering

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"In conceptual terms

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learning involves the abstraction of data into a structured representation, and the generalization of this structure into action. In more practical terms, a machine learner uses data containing _______ and _______ of the concept to be learned, and summarizes this data in the form of a __________, which is then used for __________ or __________ purposes. These can further be divided into specific tasks including __________ __________ _________, and _________. Among the many options, machine learning algorithms are chosen on the basis of the _________ and the ___________.","examples, features, model, predictive, descriptive, classification, numeric prediction, pattern detection, clustering, input data, learning task"

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