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DECISION TREE, EVALUATION METHOD, TASKS & TECHNIQUES, What is it?, : -…
DECISION TREE
How to use it?
A two-step process is followed, to build a classification model.
Model construction: describing a set of predetermined classes.
- Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute
- The set of tuples used for model construction is training set
- The model is represented as classification rules, decision trees, or mathematical formulae
Model usage: for classifying future or unknown objects
- Estimate accuracy of the model
- The known label of test sample is compared with the classified result from the model
- Accuracy rate is the percentage of test set samples that are correctly classified by the model
- Test set is independent of training set (otherwise overfitting)
Where to use it?
Often used in operations research, particularly in decision analysis, to assist discover a strategy that is most likely to achieve a goal. Also can be used for categorizing bank loan applications as safe or risky
When to use it?
A decision tree is a form of probability tree that allows you to make a choice on a given procedure. When the individual is indecisive to choose between options. Trees are a great approach to deal with these kinds of complicated decisions, which generally contain a lot of variables and are frequently involve with uncertainty.
EVALUATION METHOD
What is it?
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It helps to find the best model that represents our data and how well the chosen model will work in the future
Where to use it?
Data mining algorithms can be used to find patterns and relationships within texts, as well as patterns and relationships between texts. For example, text mining can be used for evaluation by analysing large amounts of unstructured text in open-ended survey responses
When to use it?
Model evaluation metrics are used to assess goodness of fit between model and data, to compare different models, in the context of model selection, and to predict how predictions (associated with a specific model and data set) are expected to be accurate.
How to use it?
Methods for estimating a classifier’s accuracy:
Holdout method,
random subsampling
Cross-validation
ROC Curves
TASKS & TECHNIQUES
CLUSTERING
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What is it?
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) to each other than to those in other groups (clusters).
Where to use it?
broadly used in many applications such as market research, pattern recognition, data analysis, and image processing.
When to use it?
When the data starting from a large, unstructured dataset so it can quickly organize into something more usable.
When don’t know how many or which classes your data is divided into, so clustering will answer key questions about the dataset.
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What is it?
Decision Tree Mining is a type of data mining technique that is used to build Classification Models. Like its name suggests, it creates categorization models in the shape of a tree-like structure. This is called supervised class learning.
Supervised Class
Supervision: The training data (observations, measurements, etc.) are accompanied by labels (output) indicating the class of the observations
Unsupervised Class
The class labels of training data is unknown and
given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data
The goal is to discover rules that define whether an item belongs to a particular subset or class of data.
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