I'm trying to get an estimate for the accuracy of a decision tree, Why do i get a TypeError?

Question

from sklearn.tree import DecisionTreeClassifier
import pandas as pd
from sklearn.metrics import accuracy_score

## training data (20%)
data = pd.read_csv("train.csv", usecols=[1,2,9])
X_train = pd.read_csv("train.csv", usecols=[2,9])
y_train = pd.read_csv("train.csv", usecols=[1])

dt = DecisionTreeClassifier(max_depth=6)
dt.fit(X_train, y_train)

y_predict = dt.predict(X_test)

accuracy = dt(y_test, y_predict)

i get "TypeError: 'DecisionTreeClassifier' object is not callable" even though i (mostly followed a datacamp tutorial).

Answer 1

dt(...) is attempting to "call" dt which you can't do because dt is not a function. You need a function that calculates accuracy from true and predicted labels.

Try something like this

def calculate_accuracy(y_true, y_predicted):
    num_correct = sum(map(lambda t, p: t==p, y_true, y_predicted))
    return num_correct / len(y_true)

accuracy = calculate_accuracy(y_test, y_predict)

EDIT:
The more beginner friendly version of my calculate_accuracy() would look like

def calculate_accuracy(y_true, y_predicted):
    num_correct = 0
    for i in range(len(y_true)):
        if (y_true[i] == y_predicted[i]):
            num_correct = num_correct + 1
    return num_correct / len(y_true)