Hi I want to use a simple AdaBoostClassifier on the mushroom dataset which lools smth. like:
target cap-shape cap-surface cap-color bruises odor \
3059 0 2 3 2 1 5
1953 0 5 0 3 1 5
1246 0 2 2 3 0 5
5373 1 5 2 8 1 2
413 0 5 3 9 1 3
...
using:
from sklearn.ensemble import AdaBoostClassifier
from sklearn.preprocessing import LabelEncoder
import pandas as pd
dataset = pd.read_csv('data\mushroom.csv',header=None)
dataset = dataset.sample(frac=1)
dataset.columns = ['target','cap-shape','cap-surface','cap-color','bruises','odor','gill-attachment','gill-spacing',
'gill-size','gill-color','stalk-shape','stalk-root','stalk-surface-above-ring','stalk-surface-below-ring','stalk-color-above-ring',
'stalk-color-below-ring','veil-type','veil-color','ring-number','ring-type','spore-print-color','population',
'habitat']
for label in dataset.columns:
dataset[label] = LabelEncoder().fit(dataset[label]).transform(dataset[label])
X = dataset.drop(['target'],axis=1)
Y = dataset['target']
AdaBoost = AdaBoostClassifier(base_estimator='DecisionTreeClassifier',n_estimators=400,learning_rate=0.01,algorithm='SAMME')
AdaBoost.fit(X,Y)
prediction = AdaBoost.score(Y)
print(prediction)
but this returns me:
---> 15 AdaBoost.fit(X,Y)
AttributeError: 'str' object has no attribute 'fit'
I have found the issue. As base_estimator I have set 'DecisionTreeClassifier'. THIS is a sting and has no fit() method. The AdaBoost IS NOT a string.
from sklearn.ensemble import AdaBoostClassifier
from sklearn.preprocessing import LabelEncoder
for label in dataset.columns:
dataset[label] = LabelEncoder().fit(dataset[label]).transform(dataset[label])
X = dataset.drop(['target'],axis=1)
Y = dataset['target']
AdaBoost = AdaBoostClassifier(n_estimators=400,learning_rate=0.01,algorithm='SAMME')
AdaBoost.fit(X,Y)
prediction = AdaBoost.score(X,Y)
print(prediction)
0.9182668636139832