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Brout Seminar Test #3

Terms in this set (17)

Decision Tree
is a decision support tool (and one of predictive analytics) that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm. In business, it is often used to segment customers, identify targets, and customize strategies.
Training data
The input data for inducing the predictive model.
Holdout data
Some data for which we know the value of the target variable, but which will not be used to build the model.
Accuracy
Number of correct decisions made / Total number of decisions made
Expected value (or profit)
is the weighted average of the values of the different possible outcomes, where the weight given to each value is its probability of occurrence.
Receiver Operating Characteristics (ROC) graph
A two-dimensional plot of a classifier with false positive rate on the x axis against true positive rate on the y axis
Area Under the ROC Curve (AUC)
The area under a classifier's curve expressed as a fraction of the unit square with a value ranges from zero to one.
k-means clustering
aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. As a popular unsupervised learning algorithm, this technique has been widely applied for customer segmentation's and to develop recommendation system.
Association Rules
an unsupervised learning method; that is descriptive, not predictive, method often used to discover interesting relationships hidden in a large dataset. They can answer: Which products tend to be purchased together? (market basket analysis) Of those customers who are similar to this person, what products do they tend to buy? Of those customers who have purchased this product, what other similar products do they tend to view or purchase? (Product recommendation) Which words tend to appear simultaneously in a given text data? (Word association)
Text Analytics
sometimes called text analysis or text mining, refers to the representation, processing, and modeling of textual data to derive useful insights. The source of textual data may come from news articles, literature, e-mail, user comments, web pages, social media posts, or call center transcripts.
Bag of Words
This model is a simplifying representation used in natural language processing. In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar, sentence structure, and even word order but keeping multiplicity. e.g. "I love the phone! I hate the battery!" = "I love the battery! I hate the phone!"
Term Frequency
Analysis of the word count in the document
Word Cloud
A visual representation of text data useful for quickly perceiving the most prominent terms.
Network Analysis (Semantic/Word/Text)
A visualization approach to analyze semantic network, or frame network that represents semantic relations between concepts or frequent co-occurrences between texts.
Stopwords
these are very common words in English such as the, and, of, to, when, and you.
Sentiment Analysis
refers to a group of tasks that use statistics and natural language processing to mine opinions to identify and extract subjective information from texts. In categorizing vocabularies into distinctive sentiments (i.e., positive, negative, or neutral), many different sources have been utilized. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as "angry," "sad," and "happy." Also, texts can be given a positive and negative sentiment strength score. Recently, machine learning algorithms are applied in reinforcing accuracy of classifying sentiments with using social media (e.g., Twitter) posts.
Keyword monitoring
several of keywords may be needed to be monitored in real-time bases. Then, one can set up alerts for those keywords with using (google.com/alerts, talkwalker.com/alerts)
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