54 lines
1.8 KiB
Plaintext
54 lines
1.8 KiB
Plaintext
|
# Load libraries
|
||
|
|
import pandas as pd
|
||
|
|
from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier
|
||
|
|
from sklearn.model_selection import train_test_split # Import train_test_split function
|
||
|
|
from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation
|
||
|
|
from sklearn.tree import export_graphviz
|
||
|
|
from sklearn.externals.six import StringIO
|
||
|
|
from IPython.display import Image
|
||
|
|
import pydotplus
|
||
|
|
|
||
|
|
col_names = ['age', 'sex', 'fat', 'spicy', 'hungry', 'budget']
|
||
|
|
# load dataset
|
||
|
|
pima = pd.read_csv("nazwa.csv", header=None, names=col_names)
|
||
|
|
|
||
|
|
#split dataset in features and target variable
|
||
|
|
feature_cols = ['age', 'sex', 'fat', 'spicy']
|
||
|
|
X = pima[feature_cols] # Features
|
||
|
|
y = pima.label # Target variable
|
||
|
|
|
||
|
|
# Split dataset into training set and test set
|
||
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test
|
||
|
|
|
||
|
|
# Create Decision Tree classifer object
|
||
|
|
clf = DecisionTreeClassifier()
|
||
|
|
|
||
|
|
# Train Decision Tree Classifer
|
||
|
|
clf = clf.fit(X_train,y_train)
|
||
|
|
|
||
|
|
#Predict the response for test dataset
|
||
|
|
y_pred = clf.predict(X_test)
|
||
|
|
|
||
|
|
# Model Accuracy, how often is the classifier correct?
|
||
|
|
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
|
||
|
|
|
||
|
|
dot_data = StringIO()
|
||
|
|
export_graphviz(clf, out_file=dot_data,
|
||
|
|
filled=True, rounded=True,
|
||
|
|
special_characters=True,feature_names = feature_cols,class_names=['0','1'])
|
||
|
|
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
|
||
|
|
graph.write_png('food_tree.png')
|
||
|
|
Image(graph.create_png())
|
||
|
|
|
||
|
|
|
||
|
|
# Create Decision Tree classifer object
|
||
|
|
clf = DecisionTreeClassifier(criterion="entropy", max_depth=3)
|
||
|
|
|
||
|
|
# Train Decision Tree Classifer
|
||
|
|
clf = clf.fit(X_train,y_train)
|
||
|
|
|
||
|
|
#Predict the response for test dataset
|
||
|
|
y_pred = clf.predict(X_test)
|
||
|
|
|
||
|
|
# Model Accuracy, how often is the classifier correct?
|
||
|
|
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
|