Senin, 11 Desember 2017

Matlab-Classification Learner Example Datasets

Berikut penulis sajikan dataset yang mungkin berguna untuk melakukan banyak ujicoba teknik classifier
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Fisher Iris

The Fisher Iris dataset consists of samples from three species of iris (Iris setosa, Iris virginica, and Iris versicolor). Features such as the length and the width of the sepals and petals were measured in centimeters.

Number of predictors: 4
Number of observations: 150
Number of classes: 3

Credit Rating

The credit rating dataset contains financial ratios and industry sectors information for a list of corporate customers. The response variable consists of credit ratings (AAA, AA, A, BBB, BB, B, CCC) assigned by a rating agency.

Number of predictors: 6
Number of observations: 3932
Number of classes: 7


The ovarian cancer dataset consists of high-resolution ovarian cancer data generated using the WCX2 protein array. The sample set includes 95 controls and 121 ovarian cancers.

Number of predictors: 100
Number of observations: 216
Number of classes: 2

Don’s Easter Egg

Don’s Easter Egg dataset is a simulated dataset that consists of only 2 variables and 2 classes. Since the separation boundary is highly nonlinear between the two classes, this makes for a challenging exploratory problem.

Number of predictors: 2
Number of observations: 1873
Number of classes: 2


The heart arrhythmia dataset consists of patient information and response variables that indicate the presence and absence of cardiac arrhythmia in the patient. In this medical diagnostic application, misclassifying patient as "normal" may have a more severe consequence than misclassifying a patient as "has arrhythmia."

Number of predictors: 279
Number of observations: 150
Number of classes: 16


The ionosphere dataset comprises preprocessed signals obtained from a phased array of 16 high-frequency antennas. Good returned radar signals are those showing evidence of some type of structure in the ionosphere. Bad signals are those that pass through the ionosphere.

Number of predictors: 34
Number of observations: 351
Number of classes: 2
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