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Unveiling Hidden Neural Codes: SIMPL – A Scalable and Fast Approach for Optimizing Latent Variables and Tuning Curves in Neural Population Data

Dr. Rishabh Pathak
This research paper presents SIMPL (Scalable Iterative Maximization of Population-coded Latents), a novel, computationally efficient algorithm designed to refine the estimation of latent variables and tuning curves from neural population activity. Latent variables in neural data represent essential low-dimensional quantities encoding behavioral or cognitive states, which neuroscientists seek to identify to understand brain computations better. Background and Motivation Traditional approaches commonly assume the observed behavioral variable as the latent neural code. However, this assumption can lead to inaccuracies because neural activity sometimes encodes internal cognitive states differing subtly from observable behavior (e.g., anticipation, mental simulation). Existing latent variable models face challenges such as high computational cost, poor scalability to large datasets, limited expressiveness of tuning models, or difficulties interpreting complex neural network-based functio...
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The Decision Functions

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Dr. Rishabh Pathak



1. What is the Decision Function?

  • The decision_function method is provided by many classifiers in scikit-learn.
  • It returns a continuous score for each sample, representing the classifier’s confidence or margin.
  • This score reflects how strongly the model favors one class over another in binary classification, or a more complex set of scores in multiclass classification.

2. Shape and Output of decision_function

  • For binary classification, the output shape is (n_samples,).
  • Each value is a floating-point number indicating the degree to which the sample belongs to the positive class.
  • Positive values indicate a preference for the positive class; negative values indicate a preference for the negative class.
  • For multiclass classification, the output is usually a 2D array of shape (n_samples, n_classes), providing scores for each class.

3. Interpretation of decision_function Scores

  • The sign of the value (positive or negative) determines the predicted class.
  • The magnitude represents the confidence or "distance" from the decision boundary.
  • The larger the absolute value, the more confident the model is in its classification.

Example:

print("Decision function values:\n", classifier.decision_function(X_test)[:6])
# Outputs something like:
# [4.5, -1.2, 0.3, 5.0, -3.1, ...]
  • Here, values like 4.5 or 5.0 indicate strong confidence in the positive class; -1.2 or -3.1 indicate strong preference for the negative class.

4. Relationship to Prediction Threshold

  • For binary classifiers, prediction is derived by thresholding:
  • Predicted class = positive if decision_function score > 0.
  • Predicted class = negative otherwise.
  • This threshold can be adjusted:
  • Changing threshold impacts false positives/negatives.
  • Adjusting threshold can improve metrics like precision and recall in imbalanced data.

5. Examples of Classifiers Using decision_function

  • Support Vector Machines (SVMs) use decision_function to provide margin distances from the decision boundary.
  • GradientBoostingClassifier also provides decision_function for more granular confidence.
  • Logistic regression usually does not provide decision_function but provides predict_proba instead (log odds can be considered similar).

6. Advantages of decision_function Over predict_proba

  • decision_function outputs raw scores, which might be more informative for some models.
  • These raw scores can be transformed into probabilities with calibration methods like Platt scaling.
  • For models like SVMs, predict_proba is a wrapper over decision_function with a calibration step.
  • Users can set custom thresholds on decision_function to better control classification decisions.

7. Use in Model Evaluation

  • decision_function outputs enable construction of ROC curves, which plot True Positive Rate vs False Positive Rate at different thresholds.
  • By varying the decision threshold, you can evaluate model performance across thresholds.
  • Thus, decision_function is crucial for comprehensive model assessment beyond accuracy.

8. Example Code Snippet (from the book)

from sklearn.ensemble import GradientBoostingClassifier
 
# Suppose we have a trained GradientBoostingClassifier called gbrt
print("X_test.shape:", X_test.shape)
print("Decision function shape:", gbrt.decision_function(X_test).shape)
 
print("Decision function:\n", gbrt.decision_function(X_test)[:6])

Output might be:

X_test.shape: (25, 2)
Decision function shape: (25,)
Decision function:
[4. 2.5 1.3 0.7 -1.2 -3.4]

Explanation: These values show the strength of model preference for the positive class.

9. Summary Points

Aspect

                 Details

Purpose

Measures confidence or margin in classification

Output (Binary)

Array of floats (n_samples,) indicating class preference

Output (Multiclass)

Array of floats (n_samples, n_classes) with scores per class

Interpretation

Positive = positive class, Negative = negative class; magnitude = confidence

Thresholding

Default threshold at 0 to convert to class labels

Usage

Enables custom thresholds, ROC analysis, model calibration

Example models

SVM, Gradient Boosting, some ensemble classifiers

 

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