[Python] NotFittedError: Estimator not fitted, call `fit` before exploiting the model

I am running Python 3.5.2 on a Macbook OSX 10.2.1 (Sierra).

While attempting to run some code for the Titanic Dataset from Kaggle, I keep getting the following error:

NotFittedError Traceback (most recent call last) in () 6 7 # Make your prediction using the test set and print them. ----> 8 my_prediction = my_tree_one.predict(test_features) 9 print(my_prediction) 10

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/sklearn/tree/tree.py in predict(self, X, check_input) 429 """ 430 --> 431 X = self._validate_X_predict(X, check_input) 432 proba = self.tree_.predict(X) 433 n_samples = X.shape[0]

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/sklearn/tree/tree.py in _validate_X_predict(self, X, check_input) 386 """Validate X whenever one tries to predict, apply, predict_proba""" 387 if self.tree_ is None: --> 388 raise NotFittedError("Estimator not fitted, " 389 "call fit before exploiting the model.") 390

NotFittedError: Estimator not fitted, call fit before exploiting the model.
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The offending code seems to be this:

# Impute the missing value with the median
test.Fare[152] = test.Fare.median()

# Extract the features from the test set: Pclass, Sex, Age, and Fare.
test_features = test[["Pclass", "Sex", "Age", "Fare"]].values

# Make your prediction using the test set and print them.
my_prediction = my_tree_one.predict(test_features)
print(my_prediction)

# Create a data frame with two columns: PassengerId & Survived. Survived contains your predictions
PassengerId =np.array(test["PassengerId"]).astype(int)
my_solution = pd.DataFrame(my_prediction, PassengerId, columns = ["Survived"])
print(my_solution)

# Check that your data frame has 418 entries
print(my_solution.shape)

# Write your solution to a csv file with the name my_solution.csv
my_solution.to_csv("my_solution_one.csv", index_label = ["PassengerId"])


And here is a link to the rest of the code.

Since I already have called the 'fit' function, I cannot understand this error message. Where am I going wrong? Thanks for your time.

Edit: Turns out that the problem is inherited from the previous block of code.

# Fit your first decision tree: my_tree_one
my_tree_one = tree.DecisionTreeClassifier()
my_tree_one = my_tree_one.fit(features_one, target)

# Look at the importance and score of the included features
print(my_tree_one.feature_importances_)
print(my_tree_one.score(features_one, target))


With the line: my_tree_one = my_tree_one.fit(features_one, target)

generating the error:

ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
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