Why RandomForestClassifier Predicts All Zeros and How to Fix It: Don't One-Hot Encode the Target

Troubleshooting RandomForestClassifier predicting all zeros: learn why one-hot encoding the target breaks multiclass classification and how to fix it.

RandomForestClassifier predicting all zeros usually signals a data-prep issue rather than a model defect. A common pitfall is one-hot encoding the target for a multiclass classifier. Below is a concise walkthrough of how this happens, why it breaks predictions, and how to fix it without changing your overall preprocessing flow.

Reproducing the issue

The following example mirrors a typical end-to-end pipeline: imputation, categorical encoding for features, scaling numeric features, and training a RandomForestClassifier. The crucial detail: the target is one-hot encoded before fitting the model.

import pandas as pd
import numpy as np

# Load data
frame = pd.read_csv("train.csv")
X_tr = frame.iloc[:, 1:-1].values
y_tr = frame.iloc[:, [-1]].values

frame = pd.read_csv("test.csv")
X_te = frame.iloc[:, 1:].values

# Impute missing values
from sklearn.impute import SimpleImputer
miss = SimpleImputer(missing_values=np.nan, strategy="most_frequent")
miss.fit(X_tr[:, :])
X_tr[:, :] = miss.transform(X_tr[:, :])
X_te[:, :] = miss.transform(X_te[:, :])

# Split column indices by inferred type
idx_num = []
idx_cat = []
for j in range(len(X_tr[0])):
    if type(X_tr[0][j]) == int or type(X_tr[0][j]) == float:
        idx_num.append(j)
    elif type(X_tr[0][j]) == str:
        idx_cat.append(j)

# One-hot encode categorical features
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder

pipe_x = ColumnTransformer(
    transformers=[('ohe', OneHotEncoder(), idx_cat)],
    remainder='passthrough'
)
X_tr = np.array(pipe_x.fit_transform(X_tr))
X_te = np.array(pipe_x.transform(X_te))

# One-hot encode the target (this is the source of the problem)
pipe_y = ColumnTransformer(
    transformers=[('ohe', OneHotEncoder(), [0])],
    remainder='passthrough',
    sparse_threshold=0
)
y_tr = np.array(pipe_y.fit_transform(y_tr))

# Scale numeric features
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
X_tr[:, idx_num] = scale.fit_transform(X_tr[:, idx_num])
X_te[:, idx_num] = scale.transform(X_te[:, idx_num])

# Train model
from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier(n_estimators=500, max_depth=25, random_state=42)
forest.fit(X_tr, y_tr)

y_hat = forest.predict(X_te)

# Inverse-transform the predicted one-hot target
enc_y = pipe_y.named_transformers_['ohe']
y_hat_labels = enc_y.inverse_transform(y_hat)

Why this produces all zeros

RandomForestClassifier expects class labels as the target. According to the documentation for fit:

The target values (class labels in classification, real numbers in regression).

Passing a one-hot encoded target turns a single multiclass problem into a multi-output or multilabel format. That change is not what you want here, and it can yield degenerate predictions such as vectors of zeros, which then map to a single outcome on inverse_transform. In short, one-hot encoding the target is the root cause.

The fix: keep y as labels

Do not one-hot encode y for RandomForestClassifier in this case. Leave the target as its original class labels; scikit-learn will handle it appropriately.

import pandas as pd
import numpy as np

# Load data
tbl = pd.read_csv("train.csv")
X_tr = tbl.iloc[:, 1:-1].values
y_tr = tbl.iloc[:, [-1]].values  # Keep labels as-is

tbl = pd.read_csv("test.csv")
X_te = tbl.iloc[:, 1:].values

# Impute
from sklearn.impute import SimpleImputer
imput = SimpleImputer(missing_values=np.nan, strategy="most_frequent")
imput.fit(X_tr[:, :])
X_tr[:, :] = imput.transform(X_tr[:, :])
X_te[:, :] = imput.transform(X_te[:, :])

# Identify numeric vs categorical columns
num_idx = []
cat_idx = []
for k, val in enumerate(X_tr[0]):
    if isinstance(val, int) or isinstance(val, float):
        num_idx.append(k)
    elif isinstance(val, str):
        cat_idx.append(k)

# Encode categorical features only
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder

enc_x = ColumnTransformer(
    transformers=[('ohe', OneHotEncoder(), cat_idx)],
    remainder='passthrough'
)
X_tr = np.array(enc_x.fit_transform(X_tr))
X_te = np.array(enc_x.transform(X_te))

# Scale numeric features
from sklearn.preprocessing import StandardScaler
std = StandardScaler()
X_tr[:, num_idx] = std.fit_transform(X_tr[:, num_idx])
X_te[:, num_idx] = std.transform(X_te[:, num_idx])

# Train classifier with label targets
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=500, max_depth=25, random_state=42)
rf.fit(X_tr, y_tr)

# Predict class labels directly
y_hat = rf.predict(X_te)
print(f"y_hat: {y_hat}")

If your feature encoders encounter unseen categories in X_te, they will fail. Ensure that the categories present in the test set are covered in the training data. In the sample below, the training snippet contains all fertilizer classes encountered in the test snippet, so encoding proceeds cleanly.

id,Temparature,Humidity,Moisture,Soil Type,Crop Type,Nitrogen,Potassium,Phosphorous,Fertilizer Name
0,37,70,36,Clayey,Sugarcane,36,4,5,28-28
1,27,69,65,Sandy,Millets,30,6,18,28-28
2,29,63,32,Sandy,Millets,24,12,16,17-17-17
3,35,62,54,Sandy,Barley,39,12,4,10-26-26
4,35,58,43,Red,Paddy,37,2,16,DAP
5,30,59,29,Red,Pulses,10,0,9,20-20
6,27,62,53,Sandy,Paddy,26,15,22,28-28
7,36,62,44,Red,Pulses,30,12,35,14-35-14
8,36,51,32,Loamy,Tobacco,19,17,29,17-17-17
9,28,50,35,Red,Tobacco,25,12,16,20-20
10,30,45,35,Black,Ground Nuts,20,2,19,28-28
11,25,69,42,Black,Wheat,25,12,26,30-30

Why this matters

Accidentally reformulating a multiclass problem into a multilabel or multi-output setup by encoding the target can mask issues during training and lead to misleading predictions. For RandomForestClassifier in classification mode, keep y as class labels; scikit-learn will handle the target correctly without manual one-hot encoding. For features, strings are fine because they are converted to numeric arrays via OneHotEncoder before reaching the model. A practical tip when debugging is to try the pipeline on a smaller, well-understood dataset to verify that it behaves as expected, and to make sure the training categories cover those in the test split.

Wrap-up

If a RandomForestClassifier returns a column of zeros or a single repeated class, first check whether you one-hot encoded the target. The fix is straightforward: pass the label vector directly to fit and let the library handle target encoding internally. Keep one-hot encoding for categorical features, ensure category coverage between train and test, and validate your pipeline on a smaller subset to spot setup issues early.