Source: Creator & donor: Nicholas Kushmerick nick ‘@’ ucd.ie>. Data Set Information: This dataset represents a set of possible advertisements on Internet pages. The features encode the geometry of the image (if available) as well as phrases occuring in the URL, the image’s URL and alt text, the anchor text, and words occuring near the anchor text.
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Multivariate, Text, Domain-Theory . Classification, Clustering . Real . 2500 . 10000 . 2011, Shay Cohen and Eytan Ruppin and Gideon Dror. Feature Selection Based on the Shapley Value.School of Computer Sciences Tel-Aviv University . For comparison, the grafting algorithm [Perkins et al.
2003] yields an accuracy level of approximately 75% on this dataset. 1 The Internet Ads dataset. All the algorithms did approximately the same, leading to accuracy levels between 94% and 96% with CSA …
Multivariate, Text, Domain-Theory . Classification, Clustering . Real . 2500 . 10000 . 2011, Download and read the data files into MATLAB. Dataset: Wholesale customers Data Set http :// archive.ics .uci. edu / ml / datasets /Wholesale+customers, Data Set Information: This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires.
The Iris flower data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems. It is sometimes called Anderson’s Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three …
#coding: utf-8 -*-import pandas as pd from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from sklearn.pipeline import Pipeline from sklearn.model_selection import GridSearchCV df = pd.read_csv(‘ ad -dataset/ ad .data ‘,header= None) variable_col = set(df.columns …
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