AI with Supervised Machine Learning

Supervised machine learning algorithms reveal insights, patterns, and relationships from a labeled training dataset, which already contains a known value for the target variable for each record. Because you provide the machine learning algorithm with the correct answers for a problem during training, it is able to “learn” how the rest of the features relate to the target, enabling you to reveal insights and make predictions about future outcomes based on historical data.

Classification vs. Regression

Typically, when you're dealing with supervised learning problems, you'll want to determine whether they are classification or regression problems. When working with classification problems, you map input into a finite set of classes, for example, binary classification uses two classes so they're Boolean, true/false, yes or no problems. Multiclass classification – as the name suggests – is when you are dealing with multiple classes.

Regression, on the other hand, maps input to a numerical value as opposed to a class or set of classes. For example, if you're predicting the price of a house or predicting the probability that an e-mail is spam. So not a yes or no answer, but rather a probabilistic value like 80%. Classification and regression problems can be mapped to each other however.

Regression Trees

In machine learning, predictive models are used to solve problems with the two kinds of models being classification and regression. Regression is a method for providing quantitative values and regression trees can be used to predict numerical variables. With linear regression, a model is applied globally. And unlike a regression tree, it can't easily consider interactions. In the piecewise constant model, the simplest regression tree is used where leaf nodes return a constant. And the constant is the average of the training examples' values in that partition. If data is missing, the average of the values that the branches lead to is returned.

CART Decision Tree Learning

Classification and Regression Trees, or CART, is a standard tree model used for binary splits. Whereas C4.5 and other models are used for special data, for example, C4.5 is used for non-binary splits. But the CART algorithm is similar to C4.5 and works this way. It chooses the best node to split and then splits the dataset. This is repeated recursively until a base case is reached. And the tree is pruned to reduce overfitting. Decision trees are created from top-down algorithms. And, in each state, they select a variable using metrics for identifying which variable is best for splitting the set. The metrics method varies, and here are the three common methods for splitting. Information gain or C4.5 uses entropy to determine how much information is gained after a split.

Random Forests

In the learning tree process, splitting a candidate and randomly selecting a set of sub samples is referred to as bootstrapping. And bootstrap aggregation is called bagging for short. When bagging for estimates, the process takes several random subsamples of a sample and they're allowed to overlap. Then an estimate is calculated for each sample. And that could be the mean, the variance, and so on. Then the estimates from step 2 are averaged and that's used as the estimate. In the bagging method for machine learning, subsamples are taken and a machine learning model is fitted to each subsample.

For predictions, an average of all the models is taken. A democratic process, if you will, a voting system can be used for classifiers. And the bagging method for decision trees uses CART to build each sub model. But here's the problem, subtrees are too common to make bagging a useful method for decision trees. For example, if k is a strong predictor, many of the subtrees will have k as a root node. In random forests, a random subset of features for each subtree is used. This is known as feature bagging. Here is the random forests algorithm. "n" random subsamples of the dataset are taken and a subset of features is chosen. Then a subtree is trained with subset of features using C4.5, CART, or another method. And, to make a prediction, the average result of each subtree is taken.

Logistic Regression Model

In machine learning, logistic regression is widely used for binary classification problems. It's popular in large part because it's a fairly simple algorithm that always returns a number between 0 and 1. The logistic function is used to represent a probability. The logistic regression model is a regression model used for classification. It estimates the probability of an input belonging to a class and it outputs a binary answer. And here's how the model is represented. When you're working with multiple classes with logistic regression, classes can be compared with each other. For example, you have three brands, you pick brand A against B, brand A against brand C and so on and choose the one that won the most.

Support Vector Machines (SVM)

Support Vector Machines, or SVMs, classify data into binary classes. An SVM tries to build a hyperplane to separate data. And a hyperplane is a function, a plane with one dimension fewer than the number of attributes. For example, with two attributes, that is, a 2D space, the hyperplane is a line. The optimal hyperplane has the largest margin. The margin being the distance between the hyperplane, and the close points indicated on this diagram by the dotted lines.

Kernel Methods for SVMs

Support Vector Machines, or SVM, classify data into binary classes. And a hyperplane is a function, a plane with one dimension or fewer than the number of input attributes. A soft margin hyperplane uses cost instead of a hard condition on the margin, using a hinge loss function for a hyperplane. Hinge loss refers to a function that trains maximum margin classifiers for SVMs. It works by counting how many points don't meet the condition. And generating costs based on the sum of the distances for incorrect points to the margin.

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Supervised Machine Learning AI Videos

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More information: Supervised Machine Learning offers a huge list of benefits to all. The usage of Supervised Machine Learning is such that it cannot be limited to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complex processes like Artificial Intelligence (AI), Machine Learning (ML), natural language processing, data science etc. We hope this page was helpful and provided you with some information about AI with Supervised Machine Learning. Check out our main page for more components of artificial intelligence resources.