Why Does My Scikit-Learn Classifier Lose Accuracy After Initializing Weights?
The Problem:
You've painstakingly crafted a machine learning model using Scikit-learn, only to find that initializing weights manually, instead of letting the model learn them, results in a decrease in accuracy. This can be frustrating, as you might expect giving the model a head start with pre-defined weights to improve performance.
The Scenario:
Let's imagine you're training a logistic regression classifier to classify images of cats and dogs. You decide to initialize the weights with specific values to nudge the model towards a particular bias.
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_digits
import numpy as np
# Load the dataset
digits = load_digits()
X = digits.data
y = digits.target
# Define custom weights
custom_weights = np.random.rand(X.shape[1])
# Initialize the model with custom weights
model = LogisticRegression(random_state=42,
solver='lbfgs',
multi_class='ovr',
C=1,
fit_intercept=False,
warm_start=True,
init='custom')
# Manually set the weights
model.coef_ = custom_weights.reshape(1, -1)
# Train the model
model.fit(X, y)
# Evaluate the model
print(f"Accuracy: {model.score(X, y)}")
However, you find that the accuracy of your model, even after training, is lower than when you let the model learn the weights from scratch.
Why does this happen?
-
The Curse of Overfitting: Initializing weights with pre-defined values can inadvertently introduce bias into your model, causing it to overfit the training data. The model might learn to focus on the specific features and patterns encoded in your initial weights, ignoring other potentially important aspects of the data.
-
Disrupting the Optimization Process: Gradient descent, the optimization algorithm commonly used in machine learning, relies on iteratively adjusting weights based on the model's performance on the training data. By manually setting the weights, you effectively disrupt this process, potentially leading the model astray.
-
Not Always Necessary: In many cases, allowing the model to learn weights from scratch leads to better generalization and accuracy. Predefined weights are typically used in transfer learning where you leverage a pre-trained model for a similar task, or when you have strong prior knowledge about the problem.
What to Do:
-
Start with Default Initialization: Let the model learn weights from scratch, using the default initialization methods provided by Scikit-learn. This gives the model the best chance to learn the optimal weights for your data.
-
Use Transfer Learning: If you have a pre-trained model for a similar task, you can use transfer learning to initialize weights. This approach allows you to leverage the knowledge gained from the pre-trained model while adapting it to your specific dataset.
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Understand Your Data: If you have strong prior knowledge about the relationship between features and the target variable, you can use this knowledge to guide the initialization process. However, be cautious, as this can introduce bias and overfitting if not done carefully.
Remember:
- Manually initializing weights is rarely necessary.
- Allow the model to learn the weights from scratch, unless you have a compelling reason not to.
- If you do choose to initialize weights, do so with caution and be prepared to adjust the learning rate and other hyperparameters to compensate for the introduced bias.
Further Resources:
- Scikit-learn Documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
- Understanding Gradient Descent: https://towardsdatascience.com/gradient-descent-algorithm-a-deep-dive-9f2228c8146a
- Transfer Learning with Scikit-learn: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html
