2.4 Naive Bayes Classification

2.5.1 Playing Tennis Today?

In[]:=
dataTraining={"Sunny""No","Overcast""Yes","Rainy""Yes","Sunny""Yes","Sunny""Yes","Overcast""Yes","Rainy""No","Rainy""No","Sunny""Yes","Rainy""Yes","Sunny""No","Overcast""Yes","Overcast""Yes","Rainy""No"};
In[]:=
TableForm[dataTraining,TableAlignments{Right,Center}]
Out[]//TableForm=
SunnyNo
OvercastYes
RainyYes
SunnyYes
SunnyYes
OvercastYes
RainyNo
RainyNo
SunnyYes
RainyYes
SunnyNo
OvercastYes
OvercastYes
RainyNo
In[]:=
c=Classify[dataTraining,Method{"NaiveBayes"},PerformanceGoal"Quality"]​​
Out[]=
ClassifierFunction
Input type: Nominal
Classes: No,Yes

In[]:=
c[{"Sunny"}]
Out[]=
{Yes}
In[]:=
c[{"Sunny"},"TopProbabilities"]
Out[]=
{{Yes0.58296,No0.41704}}
In[]:=
cm=ClassifierMeasurements[c,dataTraining]
Out[]=
ClassifierMeasurementsObject
Classifier: NaiveBayes
Number of test examples: 14

In[]:=
cm["Accuracy"]
Out[]=
0.714286
In[]:=
cm["ConfusionMatrixPlot"]
Out[]=
In[]:=
Map[c[#[[1]]]&,dataTraining]
Out[]=
{Yes,Yes,No,Yes,Yes,Yes,No,No,Yes,No,Yes,Yes,Yes,No}
In[]:=
Map[#[[2]]&,dataTraining]
Out[]=
{No,Yes,Yes,Yes,Yes,Yes,No,No,Yes,Yes,No,Yes,Yes,No}
In[]:=
session=StartExternalSession["Python"]
Out[]=
ExternalSessionObject
System: Python
EvaluationCount:
None
UUID: 047765f7-ce92-455b-9032-be8bff8300cb

Test
〉
In[]:=
5+6
Out[]=
11
In[]:=
dataX=Map[#[[1]]&,dataTraining]/.{"Sunny"0,"Overcast"1,"Rainy"2}
Out[]=
dataTraining
In[]:=
datay=Map[#[[2]]&,dataTraining]/.{"Yes"1,"No"0}
Out[]=
dataTraining
〉
In[]:=
import numpy as np
〉
In[]:=
X=np.array([[0],[1],[2],[0],[0],[1],[2],[2],[0],[2],[0],[1],[1],[2]])
〉
In[]:=
y=np.array([0,1,1,1,1,1,0,0,1,1,0,1,1,0])
〉
In[]:=
from sklearn.naive_bayes import GaussianNB
〉
In[]:=
​
clf=GaussianNB().fit(X,y)
〉
In[]:=
prediction=clf.predict([[0]])
prediction
Out[]=
NumericArray
Type: Integer32
Dimensions: {1}

In[]:=
Normal[%]
Out[]=
{1}
Output is: 1  Play.
The probabilities are,
〉
In[]:=
prediction=clf.predict_proba([[0]])
prediction
Out[]=
NumericArray
Type: Real64
Dimensions: {1,2}

In[]:=
Normal[%]
Out[]=
{{0.290031,0.709969}}

2.5.2 Zebra, Gorilla, Horse and Penguin

In[]:=
zebra=
,
,
,
,
;
In[]:=
gorilla=
,
,
,
,
;
In[]:=
penguin=
,
,
,
,
;
In[]:=
hourse=
,
,
,
,
;
Fig. 2.5.4. Test images