Experiment 6 Data Mining Classification Introduction Experiment
1. Experimental data
The data set contains 1439 pieces of training data, stored in the " data-train.csv " file; another 160 pieces of test data with unknown labels are stored in the " data-test.csv " file.
The training set data contains 11 (anonymous) feature attributes ( f1~f1 1 ) and 1 target attribute ( target) related to a certain wine quality . The specific fields are as follows: #data-train.csv
The specific fields of the test data are as follows: #data-test.csv
Among them, the target attribute, that is, the target field is unknown and needs to be modeled and predicted.
2. Purpose of experiment
The purpose of this experiment is to use the machine learning classification algorithm to build a classifier model based on the training set, and then predict the classification results of all samples in the test set, that is, the target value of the test sample : A, B