Label Encoder
Encode categorical labels to integers, one-hot vectors, or frequency counts for machine learning classification tasks. View class mapping, distribution, and export as CSV or JSON.
Input Labels
Encoding Strategies for Categorical Data in Machine Learning
Machine learning algorithms operate on numerical data. Categorical labels — text values like class names, sentiment categories, or product types — must be converted to numbers before they can be used as model inputs or targets. The choice of encoding strategy significantly affects both model accuracy and training efficiency.
Integer Encoding
Low MemoryBest for: Tree models, embeddings
Maps each class to a unique integer. Compact, order-free. Ideal for target variables in classification and for categorical inputs to tree-based models like XGBoost.
One-Hot Encoding
No Ordinal BiasBest for: Linear models, neural nets
Creates one binary column per class. Eliminates implied ordinal relationships. Preferred for categorical features in logistic regression and MLPs.
Frequency Encoding
High CardinalityBest for: High cardinality features
Replaces labels with their occurrence count. Useful when class frequency correlates with the target, and when one-hot would create too many columns.
Avoiding Common Label Encoding Mistakes
Incorrect encoding is a frequent source of subtle model bugs. The most common mistake is applying integer encoding to nominal categorical features in linear models. When you encode {"red": 0, "green": 1, "blue": 2}, a linear model interprets blue as mathematically twice green — a relationship that has no meaning. Use one-hot encoding for nominal features in any linear or distance-based model.
| Scenario | Recommended Encoding | Why |
|---|---|---|
| Classification target (y) | Integer | Model expects a single integer class index |
| Nominal feature, linear model | One-Hot | Avoids false ordinal relationship |
| Ordinal feature (low/med/high) | Integer (manual) | Define order explicitly to preserve meaning |
| Input to tree model (XGBoost) | Integer or One-Hot | Trees don't assume ordinality; either works |
| High-cardinality category (>50) | Frequency or Embedding | One-hot would create too many sparse columns |
| Neural network input | Integer → Embedding layer | Learns a dense representation automatically |
Implementing Label Encoding in Python
scikit-learn LabelEncoder
from sklearn.preprocessing import LabelEncoder le = LabelEncoder() y_train = le.fit_transform(labels_train) # Transform new data (no refit!) y_test = le.transform(labels_test) # Inverse transform original = le.inverse_transform([0, 1, 2])
scikit-learn OneHotEncoder
from sklearn.preprocessing import OneHotEncoder ohe = OneHotEncoder(sparse_output=False) X_enc = ohe.fit_transform(X[["category"]]) # Get feature names cols = ohe.get_feature_names_out(["category"]) # Drop first to avoid multicollinearity ohe2 = OneHotEncoder(drop="first")
Pandas Categorical
import pandas as pd # Integer encoding df["label_int"] = pd.Categorical(df["label"]).codes # One-hot encoding df_ohe = pd.get_dummies(df["label"], prefix="cls") # Frequency encoding freq = df["label"].value_counts() df["label_freq"] = df["label"].map(freq)
Preventing Data Leakage
# WRONG: fit on all data le.fit(all_labels) # CORRECT: fit on train only le.fit(train_labels) train_enc = le.transform(train_labels) val_enc = le.transform(val_labels) # no refit # Save encoder for inference import joblib joblib.dump(le, "label_encoder.pkl")
Frequently Asked Questions
What is label encoding and when should I use it?
Label encoding converts categorical text labels (like 'cat', 'dog', 'bird') into integer values (0, 1, 2). You should use it when your target variable is categorical and you are working with tree-based models (decision trees, random forests, gradient boosting) or neural networks with an embedding layer. Avoid integer encoding for ordinal features in linear models, because the numeric order implies a mathematical relationship between classes that does not actually exist — use one-hot encoding for those cases instead.
What is the difference between integer encoding, one-hot encoding, and frequency encoding?
Integer encoding maps each class to a unique integer (cat=0, dog=1, bird=2). It is compact and works well with tree models. One-hot encoding creates a binary column for each class — each row has exactly one '1' and all other columns are '0'. It eliminates false ordinal relationships but expands dimensionality. Frequency encoding replaces each label with how many times it appears in the dataset. It captures some statistical signal and is useful when class frequency correlates with the target variable.
How does this tool handle class ordering?
Classes are sorted alphabetically before assigning integer codes. This makes the mapping deterministic and reproducible — the same input always produces the same mapping. In scikit-learn's LabelEncoder, the default behavior also sorts classes alphabetically. If you need a custom ordering (e.g., 'low'=0, 'medium'=1, 'high'=2 for ordinal data), you should define the mapping explicitly rather than relying on any automated tool.
Can I encode multi-class problems with this tool?
Yes. This tool handles any number of unique classes — binary (2 classes), multi-class (3–100+), and even very high-cardinality categorical features. For high-cardinality features with hundreds of classes, one-hot encoding will produce very wide matrices. In those cases, frequency encoding or embedding-based encoding is more practical. The one-hot preview is limited to 10 rows and the column display fits best for up to ~10 classes.
How do I use this encoding in Python with scikit-learn?
For target labels use sklearn.preprocessing.LabelEncoder — it sorts alphabetically just like this tool. For features, use sklearn.preprocessing.OrdinalEncoder for integer encoding, or sklearn.preprocessing.OneHotEncoder for one-hot encoding. To replicate frequency encoding, use a pandas value_counts() map: df['col'] = df['col'].map(df['col'].value_counts()). Always fit encoders on your training set only, then transform your validation and test sets separately to prevent data leakage.