# scikit-learn RandomForestClassifier always hits 100% test accuracy

I have been playing with a toy problem to compare the performance and behavior of several scikit-learn classifiers.

Brief, I have one continuous variable X (which contains two samples of size N, each drawn from a distinct normal distributions) and a corresponding label y (either 0 or 1).

X is built as follows:

# Subpopulation 1
s1 = np.random.normal(mu1, sigma1, n1)
l1 = np.zeros(n1)

# Subpopulation 2
s2 = np.random.normal(mu2, sigma2, n2)
l2 = np.ones(n2)

# Merge the subpopulations
X = np.concatenate((s1, s2), axis=0).reshape(-1, 1)
y = np.concatenate((l1, l2))


n1, n2: number of data points in each sub-population; mu1, sigma1, mu2, sigma1: mean and standard deviation of each population from which the sample is drawn.

I then split X and y into training and test set:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.25)


And then I fit a series of models, for instance:

from sklearn import svm
clf = svm.SVC()

# Fit
clf.fit(X_train, y_train)


or, alternatively (full list in the table at the end):

from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()

# Fit
rfc.fit(X_train, y_train)


For all models, I then calculate the accuracy on the training and the test sets. For this I implemented following function:

def apply_model_and_calc_accuracies(model):
# Calculate accuracy on training set
y_train_hat = model.predict(X_train)
a_train = 100 * sum(y_train == y_train_hat) / y_train.shape
# Calculate accuracy on test set
y_test_hat = model.predict(X_test)
a_test = 100 * sum(y_test == y_test_hat) / y_test.shape
# Return accuracies
return a_train, a_test


I compare the algorithms by changing n1, n2, mu1, sigma1, mu2, sigma1 and checking the accuracies of the training and test sets. I initialize the classifiers with their default parameters.

To make a long story short, the Random Forest Classifier always scores 100% accuracy on the test test, no matter what parameters I set.

If, for instance, I test the following parameters:

n1 = n2 = 250
mu1 = mu2 = 7.0
sigma1 = sigma2 = 3.0,


I merge two completely overlapping subpopulations into X (they still have the correct label y associated to them). My expectation for this experiment is that the various classifiers should be completely guessing, and I would expect a test accuracy of around 50%.

In reality, this is what I get:

| Algorithm                  | Train Accuracy % | Test Accuracy % |
|----------------------------|------------------|-----------------|
| Support Vector Machines    |  56.3            |  42.4           |
| Logistic Regression        |  49.1            |  52.8           |
| Stochastic Gradien Descent |  50.1            |  50.4           |
| Gaussian Naive Bayes       |  50.1            |  52.8           |
| Decision Tree              | 100.0            |  51.2           |
| Random Forest              | 100.0            | *100.0*         |
| Multi-Layer Perceptron     |  50.1            |  49.6           |


I don't understand how this is possible. The Random Forest classifier never sees the test set during training, and still classify with 100% accuracy.

Thanks for any input!

Upon request, I paste my code here (with only two of the originally tested classifiers and less verbose outputs).

import numpy as np
import sklearn
import matplotlib.pyplot as plt

# Seed
np.random.seed(42)

# Subpopulation 1
n1 = 250
mu1 = 7.0
sigma1 = 3.0
s1 = np.random.normal(mu1, sigma1, n1)
l1 = np.zeros(n1)

# Subpopulation 2
n2 = 250
mu2 = 7.0
sigma2 = 3.0
s2 = np.random.normal(mu2, sigma2, n2)
l2 = np.ones(n2)

# Display the data
plt.plot(s1, np.zeros(n1), 'r.')
plt.plot(s2, np.ones(n1), 'b.')

# Merge the subpopulations
X = np.concatenate((s1, s2), axis=0).reshape(-1, 1)
y = np.concatenate((l1, l2))

# Split in training and test sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.25)
print(f"Train set contains {X_train.shape} elements; test set contains {X_test.shape} elements.")

# Display the test data
X_test_0 = X_test[y_test == 0]
X_test_1 = X_test[y_test == 1]
plt.plot(X_test_0, np.zeros(X_test_0.shape), 'r.')
plt.plot(X_test_1, np.ones(X_test_1.shape), 'b.')

# Define a commodity function
def apply_model_and_calc_accuracies(model):
# Calculate accuracy on training set
y_train_hat = model.predict(X_train)
a_train = 100 * sum(y_train == y_train_hat) / y_train.shape
# Calculate accuracy on test set
y_test_hat = model.predict(X_test)
a_test = 100 * sum(y_test == y_test_hat) / y_test.shape
# Return accuracies
return a_train, a_test

# Classify

# Use Decision Tree
from sklearn import tree
dtc = tree.DecisionTreeClassifier()

# Fit
dtc.fit(X_train, y_train)

# Calculate accuracy on training and test set
a_train_dtc, a_test_dtc = apply_model_and_calc_accuracies(dtc)

# Report
print(f"Training accuracy = {a_train_dtc}%; test accuracy = {a_test_dtc}%")

# Use Random Forest
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()

# Fit
rfc.fit(X, y)

# Calculate accuracy on training and test set
a_train_rfc, a_test_rfc = apply_model_and_calc_accuracies(rfc)

# Report
print(f"Training accuracy = {a_train_rfc}%; test accuracy = {a_test_rfc}%")

• I have a couple of suggestions which might help debug your problem. 1) train a random forest with a low number of estimators, as it should essentially make it a decision tree, and see what happens then 2) you generated overlapping data, but try to create identical data that have both classes Apr 6, 2020 at 10:19
• Following your first suggestion, I went from 100 estimators (the default) down to 10, and indeed the test accuracy went down to 96%. With 1 estimator it goes even lower to 86.1%. So, the training (and testing) procedure seem to be correct. I am not completely sure I understood your second point, however. Apr 6, 2020 at 10:41
• You use the same parameters to generate your data, but you don't necessarily generate the exact same data. What I mean is create one dataset, label it with 0, then make a copy of it but label it with 1. That way, your model must guess Apr 6, 2020 at 11:08
• Indeed, with two copies of the same sample once labeled with 0 and once with 1, the Random Forest classifier reaches a test accuracy of 43.2%. So everything seems to behave correctly. Now I just need to wrap my head around the idea that the Random Forest classifier can correctly label test examples from two distinct sets coming from the exact same distribution. Apr 6, 2020 at 12:51

rfc.fit(X, y) should be rfc.fit(X_train, y_train)

You are simply memorizing the entire dataset with RandomForestClassifier.

• Sorry, everyone! Apr 8, 2020 at 10:12

I am debugging your code and I don't get those results, if I copy paste your code and I run it I get:

from sklearn.metrics import accuracy_score
accuracy_score(rfc.predict(X_test),y_test)

>>>0.488

y_test_hat = rfc.predict(X_test)
100 * sum(y_test == y_test_hat) / y_test.shape
>>> 48.8

apply_model_and_calc_accuracies(rfc)
>>> (100.0, 48.8)


Could you share the exact line that you make in order to get those results. It is for sure a debugging error not a conceptual one.

• After fitting the model, I call my apply_model_and_calc_accuracies(rfc) with the fitted model RandomForestClassifier (rfc). Apr 6, 2020 at 13:33
• @AaronPonti could you provide the full script? For me right now seems fine Apr 6, 2020 at 16:07
• I edited my original post to add a trimmed-down version of the code that shows the problem. Apr 7, 2020 at 17:42
• as a result of the DT[Training accuracy = 100.0%; test accuracy = 44.0%] and for the RF[Training accuracy = 93.33333333333333%; test accuracy = 94.4%] which makes completely sense for me Apr 7, 2020 at 18:11