What is difference between scikit-learn's MLPClassifier vs Keras Sequential Model?

MLPClassifier stands for Multi-Layer Perceptron Classifier, part of Scikit-learn's neural_network module.

It’s a high-level abstraction for a feedforward neural network that:

Trains using backpropagation

Supports multiple hidden layers

Uses common activation functions like 'relu', 'tanh'

Is optimized using solvers like 'adam' or 'sgd'

Is focused on classification problems

from sklearn.neural_network import MLPClassifier

clf = MLPClassifier(

    hidden_layer_sizes=(64, 32),  # Two hidden layers: 64 and 32 neurons

    activation='relu',            # Activation function

    solver='adam',                # Optimizer

    max_iter=300,                 # Max training iterations

    random_state=42

)

clf.fit(X_train, y_train)

What is Sequential (Keras) Model?

The model you showed uses Keras (TensorFlow backend) and gives you lower-level control over:

Architecture design (layers, units, activations)

Optimizer details

Training loop customization

Loss functions and metrics

Fine-tuning and regularization options

Below is Kera's example 

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

model = Sequential([

    Dense(64, activation='relu', input_shape=(input_dim,)),

    Dense(32, activation='relu'),

    Dense(1, activation='sigmoid')

])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

model.fit(X_train, y_train, epochs=10, batch_size=32)

Below are the key differences between MLPClassifier and Sequential 

Feature MLPClassifier (Scikit-learn) Sequential Model (Keras)

Level of Control High-level abstraction Low-level, full control

Custom Layers/Design Limited (Dense-only) Highly flexible (any architecture)

Use Case Quick prototyping/classification Production-ready, deep customization

Loss Functions Handled internally You explicitly choose (binary_crossentropy, etc.)

Training Control .fit(X, y) only Full control over training loop

Model Evaluation score, predict_proba, etc. evaluate, predict, etc.

Built-in Regularization Basic (L2, dropout via early stopping) Advanced (dropout, batch norm, callbacks, etc.)

Performance Tuning Less flexible Very flexible (custom metrics, callbacks, etc.)

When to use what? 

Scenario Use MLPClassifier Use Keras Sequential Model

Simple classification task ✅ Quick and effective ❌ Overkill

Need advanced model architecture ❌ Limited ✅ Full control

Custom training process, callbacks, tuning ❌ ✅

Interoperability with other scikit-learn tools (e.g., pipelines) ✅ ❌

You want to deploy a deep learning model ❌ ✅

In summary, 

Use MLPClassifier for quick experiments and classic machine learning pipelines.

Use Keras Sequential API when:

You want deep learning capabilities

You need fine-tuned control

You're building complex architectures

Let me know if you'd like a side-by-side example for the same dataset using both!