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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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Predicting Probabilities

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Dr. Rishabh Pathak

1. What is Predicting Probabilities?

  • The predict_proba method estimates the probability that a given input belongs to each class.
  • It returns values in the range [0, 1], representing the model's confidence as probabilities.
  • The sum of predicted probabilities across all classes for a sample is always 1 (i.e., they form a valid probability distribution).

2. Output Shape of predict_proba

  • For binary classification, the shape of the output is (n_samples, 2):
  • Column 0: Probability of the sample belonging to the negative class.
  • Column 1: Probability of the sample belonging to the positive class.
  • For multiclass classification, the shape is (n_samples, n_classes), with each column corresponding to the probability of the sample belonging to that class.

3. Interpretation of predict_proba Output

  • The probability reflects how confidently the model believes a data point belongs to each class.
  • For example, in binary classification:

Input Sample

Predicted Probability (Negative Class)

Predicted Probability (Positive Class)

1

0.2

0.8

2

0.9

0.1
  • The model predicts positive class if the positive class probability is greater than a threshold (default 0.5).

4. Relation to Thresholding and Classification

  • The default threshold for making classification decisions is 0.5:
  • If predict_proba for positive class > 0.5, sample is classified as positive.
  • Otherwise, it is classified as negative.
  • You can adjust this threshold depending on the problem, which affects false positive and false negative rates.
  • Adjusting thresholds can optimize metrics like precision, recall, F-score, especially on imbalanced datasets.

5. Calibration of Probability Estimates

  • Not all models produce well-calibrated probabilities.
  • A calibrated model outputs probabilities that closely match true likelihoods.
  • Example of a poor calibration: a decision tree grown to full depth might assign probability 1 or 0, but be often wrong.
  • Calibration can be improved using methods like:
  • Platt scaling
  • Isotonic regression
  • Reference: Paper by Niculescu-Mizil and Caruana, “Predicting Good Probabilities with Supervised Learning”.

6. Examples Using predict_proba (from the book)

  • Using a GradientBoostingClassifier on toy datasets:
# Suppose gbrt is a trained GradientBoostingClassifier
print("Shape of probabilities:", gbrt.predict_proba(X_test).shape)
# Output:
# Shape of probabilities: (n_samples, 2)
 
print("Predicted probabilities:\n", gbrt.predict_proba(X_test[:6]))
  • Output shows actual predicted probabilities for each class:
[[0.1 0.9]
[0.8 0.2]
[0.7 0.3]
...
]
  • The first column corresponds to the first class probability, the second column to the second class.

7. Advantages of predict_proba

  • Provides interpretable uncertainty estimates in terms of probabilities.
  • Useful for decision making where probabilistic thresholds are preferable to hard decisions.
  • Can be integrated into pipelines that weigh risks (e.g., medical diagnosis, fraud detection).
  • Helps in ranking samples by probability to prioritize further analysis.

8. Relationship Between predict_proba and decision_function

  • Some classifiers implement both decision_function and predict_proba:
  • decision_function returns raw scores or margins.
  • predict_proba converts these scores to probabilities.
  • Probabilities are usually obtained by applying a logistic function or softmax on the decision function scores.
  • Calibrated models provide better probability estimates compared to raw scores alone,.

9. Practical Considerations

  • When probabilities are needed (e.g., for risk assessment), prefer models supporting predict_proba.
  • Be cautious that probabilities are only as good as model calibration.
  • Always validate probabilities with calibration plots or metrics like Brier score.

10. Summary Table

Aspect

Details

Purpose

Provides class membership probabilities

Output Shape

Binary: (n_samples, 2), Multiclass: (n_samples, n_classes)

Values

Probabilities between 0 and 1, sum to 1 per sample

Default threshold

0.5 for binary classification

Calibration

Models may need calibration for accurate probabilities

Applications

Threshold tuning, risk assessment, ranking predictions

Relation

Derived from decision_function scores via logistic or softmax

Example Models

GradientBoostingClassifier, Logistic Regression, Random Forest

 

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