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Decision Trees |
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Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the items target value. |
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Decision Trees |
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It is one of the predictive modelling approaches used in statistics, data mining and machine learning. |
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Decision Trees |
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Tree models where the target variable can take a finite set of values are called classification trees. |
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Decision Trees |
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In these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. |
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Decision |
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Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. |
| 2 |
Simple Regression |
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In statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. |
| 2 |
Simple Regression |
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In other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible. |
| 3 |
Logistic Regression |
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Logistic regression was developed by statistician David Cox in 1958[2][3](although much work was done in the single independent variable case almost two decades earlier). |
| 3 |
Logistic Regression |
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The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). |
| 3 |
Logistic Regression |
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As such it is not a classification method. |
| 3 |
Logistic Regression |
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It could be called a qualitative response/discrete choice model in the terminology of economics. |
| 4 |
Cluster analysis |
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Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). |
| 4 |
Cluster analysis |
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It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. |
| 4 |
Cluster analysis |
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Cluster analysis itself is not one specific algorithm, but the general task to be solved. |
| 4 |
Cluster analysis |
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It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. |
| 5 |
Association rule learning |
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Association rule learning is a method for discovering interesting relations between variables in large databases. |
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Association rule learning |
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It is intended to identify strong rules discovered in databases using different measures of interestingness. |
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Association rule learning |
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Based on the concept of strong rules, Rakesh Agrawal et al.[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. |
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Association rule learning |
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For example, the rule {onions, potatoes} => {burger} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. |