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On this iris dataset:

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Build a decision tree classification model, where dependent variable is “Species” and independent variable is “Sepal.Length”.

Sol:

y = iris[[‘Species’]]

x = iris[[‘Sepal.Length’]]

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.4)

from sklearn.tree import DecisionTreeClassifier

dtc = DecisionTreeClassifier()

dtc.fit(x_train,y_train)

y_pred=dtc.predict(x_test)

from sklearn.metrics import confusion_matrix

confusion_matrix(y_test,y_pred)

(22+7+9)/(22+2+0+7+7+11+1+1+9)

Code explanation:

We start off by extracting the independent variable and dependent variable:

y = iris[[‘Species’]]

x = iris[[‘Sepal.Length’]]

Then, we go ahead and divide the data into train and test set:

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.4)

After that, we go ahead and build the model:

from sklearn.tree import DecisionTreeClassifier

dtc = DecisionTreeClassifier()

dtc.fit(x_train,y_train)

y_pred=dtc.predict(x_test)

Finally, we build the confusion matrix:

from sklearn.metrics import confusion_matrix

confusion_matrix(y_test,y_pred)

(22+7+9)/(22+2+0+7+7+11+1+1+9)

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