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Classification

Classification#

This example walks through a complete binary/multi-class classification workflow using DeepTab — from generating data to evaluating a trained model.

Setup#

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

from deeptab.models import MambularClassifier

Generate data#

We create a synthetic tabular dataset with 1 000 samples and 5 numeric features. The continuous target is bucketed into four quartile classes to form a multi-class classification problem.

np.random.seed(42)

n_samples, n_features = 1000, 5
X = np.random.randn(n_samples, n_features)
y_continuous = np.dot(X, np.random.randn(n_features)) + np.random.randn(n_samples)

df = pd.DataFrame(X, columns=[f"feature_{i}" for i in range(n_features)])
df["target"] = pd.qcut(y_continuous, q=4, labels=False)

Split#

X = df.drop(columns=["target"])
y = df["target"].values

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

Train#

Instantiate MambularClassifier with default hyperparameters and fit on the training split. max_epochs is kept small here for illustration.

model = MambularClassifier()
model.fit(X_train, y_train, max_epochs=10)

Evaluate#

metrics = model.evaluate(X_test, y_test)
print(metrics)

Note

Replace MambularClassifier with any other classifier from deeptab.models (e.g. ResNetClassifier, FTTransformerClassifier) without changing any other line.

Using your own data#

Replace the synthetic data block with your own DataFrame. DeepTab detects column types automatically — no manual encoding needed:

import pandas as pd
from sklearn.model_selection import train_test_split
from deeptab.models import MambularClassifier

df = pd.read_csv("your_data.csv")
X = df.drop(columns=["target"])
y = df["target"].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = MambularClassifier()
model.fit(X_train, y_train, max_epochs=50)
print(model.evaluate(X_test, y_test))

Next steps#

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