IrisRecognitionWithML.NET

About Dataset

Source of Dataset

• Abstract: Famous database; from Fisher, 1936
• Creator: R.A. Fisher
• Donor: Michael Marshall (MARSHALL%PLU '@' io.arc.nasa.gov)

Data Set Information

This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.

Predicted attribute: class of iris plant.

This is an exceedingly simple domain.

This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick '@' espeedaz.net). The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" where the errors are in the second and third features.

Attribute Information

1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica

You can download this dataset at https://archive.ics.uci.edu/ml/machine-learning-databases/iris/

About this project

ML.NET version	API type	Status	App Type	Data type	Scenario	ML Task	Algorithms
v1.5.0	Dynamic API	Up-to-date	Console app	.csv file	Iris flowers classification	Multi-class classification	Many

Result

#	Trainer	MicroAccuracy	MacroAccuracy	Duration	#Iteration
1	AveragedPerceptronOva	0.9433	0.9514	2.6	1
2	SdcaMaximumEntropyMulti	0.9719	0.9773	5.0	2
3	LightGbmMulti	0.9616	0.9688	2.0	3
4	SymbolicSgdLogisticRegressionOva	0.5934	0.5600	1.3	4
5	FastTreeOva	0.9458	0.9537	3.5	5
6	LinearSvmOva	0.9545	0.9611	1.0	6
7	LbfgsLogisticRegressionOva	0.8928	0.9167	1.2	7
8	SgdCalibratedOva	0.7102	0.7372	1.5	8
9	FastForestOva	0.9458	0.9544	3.0	9
10	LbfgsMaximumEntropyMulti	0.9554	0.9637	0.9	10
11	SdcaMaximumEntropyMulti	0.6686	0.6667	0.8	11
12	LightGbmMulti	0.9501	0.9574	2.2	12
13	LbfgsMaximumEntropyMulti	0.9399	0.9489	0.8	13
14	SdcaMaximumEntropyMulti	0.9600	0.9614	0.8	14
15	LightGbmMulti	0.9529	0.9627	1.4	15
16	LbfgsMaximumEntropyMulti	0.9486	0.9556	0.7	16
17	SdcaMaximumEntropyMulti	0.9176	0.9285	0.7	17
18	LightGbmMulti	0.9529	0.9627	1.1	18
19	LbfgsMaximumEntropyMulti	0.9501	0.9574	0.8	19
20	SdcaMaximumEntropyMulti	0.8690	0.8890	1.1	20
21	LightGbmMulti	0.9684	0.9769	3.0	21
22	LbfgsMaximumEntropyMulti	0.9588	0.9642	0.8	22

Experiment Results

Summary
ML Task: multiclass-classification
Dataset: D:\Github\IrisRecognitionWithML.NET\iris.csv
Label : variety
Total experiment time : 35.99731 Secs
Total number of models explored: 22

Top 5 models explored

#	Trainer	MicroAccuracy	MacroAccuracy	Duration	#Iteration
1	SdcaMaximumEntropyMulti	0.9719	0.9773	5.0	1
2	LightGbmMulti	0.9684	0.9769	3.0	2
3	LightGbmMulti	0.9616	0.9688	2.0	3
4	SdcaMaximumEntropyMulti	0.9600	0.9614	0.8	4
5	LbfgsMaximumEntropyMulti	0.9588	0.9642	0.8	5