SHAP - Common questions

 

Q1: If SHAP Tests Prediction with Only One Feature, How Does It Handle the Other Two?

SHAP doesn’t simply set the other features to 0. Instead, it marginalizes them, meaning it replaces them with their expected (average) value from the dataset.

🔹 Example: Suppose we have 3 features in an XGBoost model predicting loan approval:

• Income
• Credit Score
• Age

Now, to estimate SHAP for Income, SHAP asks:
"How does the prediction change when we include Income versus when we exclude it?"

To exclude Income, SHAP replaces it with a typical value from the dataset (not 0, because that could be unrealistic). This is done in two ways:
1️⃣ Mean Imputation: Replace missing features with their average.
2️⃣ Conditional Expectation: Replace missing features with values drawn from similar data points.

💡 Example Calculation:

• Suppose Income = $50K, Credit Score = 750, and Age = 30.
• If we remove "Income", we use an expected Income value, say $45K, based on other people with similar Credit Scores & Age.
• Then, the model predicts without using the real "Income" value.

So, instead of setting missing features to 0, SHAP replaces them with realistic values.

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Q2: What Does It Mean by Testing Different Orders of Features?

🔹 Why does SHAP check different feature orders?
Imagine we want to measure how much "Income" contributes to a loan approval decision. But the contribution of Income depends on whether we already know the Credit Score.

• If we first add Income, the model might increase the approval probability a lot.
• If we first add Credit Score, then adding Income later might increase the probability only a little (since Credit Score already explained much of the variation).

How SHAP Handles This?

SHAP calculates the contribution of each feature across all possible feature orders and averages the effect.

🔹 Example Feature Orders Tested:
1️⃣ Income → Credit Score → Age
2️⃣ Credit Score → Income → Age
3️⃣ Age → Income → Credit Score
4️⃣ ... (All possible orders)

💡 Why is this important?

• Some features might appear more or less important depending on whether other features were added first.
• By averaging over all possible orders, SHAP gives a fair contribution score to each feature.

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Final Summary

✅ SHAP does not set missing features to 0 but replaces them with typical values from the dataset.
✅ SHAP tests all possible feature orders because feature importance depends on what is already known.
✅ By averaging across orders, SHAP provides a fair, unbiased contribution score for each feature.

How Does SHAP Find the Contribution of Each Feature?

 

SHAP (SHapley Additive exPlanations) is based on game theory. Imagine your model is a team game, where each feature is a player, and the goal is to predict an outcome (e.g., loan approval, fraud detection).

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📌 Step-by-Step Explanation

Step 1: Think of Each Feature as a Player in a Team

Let’s say we have a model predicting loan approval, with these features:

• Income
• Credit Score
• Age

Each feature contributes to the final prediction, just like a player contributes to a team’s success.

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Step 2: Play the Game with Different Combinations of Players

SHAP tests different combinations of features by adding or removing them from the model and checking how much they change the prediction.

Features Used	Model Prediction (Loan Approval %)
No features (baseline)	50%
Income only	70%
Income + Credit Score	85%
Income + Credit Score + Age	90%

Now, SHAP calculates how much each feature increased the prediction.

• Income alone increased approval from 50% → 70% (+20%).
• Credit Score further increased it from 70% → 85% (+15%).
• Age added a smaller increase from 85% → 90% (+5%).

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Step 3: Average the Contribution Across All Possible Orders

SHAP doesn’t just test one order of features. It tries all possible orders and averages the contributions.

Example orderings: 1️⃣ Income → Credit Score → Age
2️⃣ Credit Score → Income → Age
3️⃣ Age → Income → Credit Score
... (all possible ways)

By doing this, SHAP finds the true average contribution of each feature regardless of order.

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🚀 Final Formula (Not Too Math-Heavy)

For each feature $X_i$, SHAP computes:

$$SHAP(X_i) = \sum \left[ \text{(Model prediction with feature)} - \text{(Model prediction without feature)} \right]$$

It averages this over all possible feature orderings.

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📌 Key Takeaways

✅ SHAP = How much a feature changed the prediction
✅ Tries all combinations of features to avoid bias
✅ Averages contributions from different feature orderings
✅ Higher SHAP value = More important feature

Would you like a real example with SHAP visualizations? 🚀

Information Value (IV) vs SHAP Values: Key Differences

 

Both Information Value (IV) and SHAP values help in understanding the importance of features in a model, but they have different applications and interpretations.

Feature	Information Value (IV)	SHAP Values (SHapley Additive Explanations)
Purpose	Measures the predictive power of a feature in a classification model.	Explains how each feature contributes to an individual model prediction.
Type of Importance	Global: Ranks features based on their overall impact on predictions.	Local + Global: Provides importance per prediction and overall feature ranking.
Interpretation	Higher IV means a feature separates target classes well.	Positive/negative SHAP values show how much a feature pushes the prediction up or down.
Works With	Logistic Regression, Credit Scoring Models.	Any ML model (Tree-based models, Deep Learning, etc.).
Mathematical Basis	Weight of Evidence (WOE): Measures how well a feature separates the target classes.	Game Theory (Shapley Values): Measures each feature’s contribution to the prediction.
Use Case	Feature selection for classification problems (e.g., credit risk models).	Model explainability for black-box models (e.g., random forests, XGBoost, neural networks).

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1️⃣ What is Information Value (IV)?

Information Value (IV) is used to measure how predictive a feature is in separating two classes (e.g., fraud vs. non-fraud, churn vs. non-churn). It is derived from Weight of Evidence (WOE).

Formula for IV

$$IV = \sum \left( \text{WOE} \times (\% \text{ Good } - \% \text{ Bad }) \right)$$

Where:

• WOE (Weight of Evidence) = $\ln (\frac{\% \text{ Good }}{\% \text{ Bad }})$
• Good and Bad refer to class distributions (e.g., non-churn vs. churn)

How to Interpret IV

IV Value	Predictive Power
< 0.02	Not useful
0.02 - 0.1	Weak predictor
0.1 - 0.3	Medium predictor
0.3 - 0.5	Strong predictor
> 0.5	Very strong predictor

Example of IV Calculation

Consider a credit risk model where we analyze the feature "Credit Score" for predicting default (Yes/No).

Credit Score Bin	% Good (No Default)	% Bad (Default)	WOE	IV Contribution
300-500	10%	50%	-1.61	0.64
500-700	40%	40%	0.00	0.00
700-850	50%	10%	1.61	0.64

Total IV = 1.28, meaning "Credit Score" is a very strong predictor.

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2️⃣ What is SHAP (Shapley Values)?

SHAP values explain how much each feature contributes to the model’s prediction for a given instance.

Key Idea

• The SHAP value of a feature tells how much it increases or decreases the model’s prediction compared to the average.
• It is based on game theory, treating each feature as a "player" contributing to the outcome.

How to Interpret SHAP

• Positive SHAP: Increases the predicted value.
• Negative SHAP: Decreases the predicted value.
• Magnitude: The larger the SHAP value, the more significant the feature’s contribution.

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Example: Comparing IV vs. SHAP in a Credit Model

Imagine we are predicting loan default (Yes/No) using Age, Credit Score, and Income.

1️⃣ Information Value (IV)

Feature	IV Value	Importance
Credit Score	0.75	Very strong
Income	0.40	Strong
Age	0.20	Medium

Interpretation:

• Credit Score is the most important predictor at a global level.
• IV does not show how these features affect individual predictions.

2️⃣ SHAP Values for a Specific Prediction

Example: Predicting Default Probability for a Person

• Person A: Age = 45, Credit Score = 600, Income = $50,000
• Model Output: Predicted Probability of Default = 0.65 (65%)

Feature	SHAP Value	Contribution to Prediction
Credit Score	+0.20	Increases default risk
Income	-0.15	Decreases default risk
Age	+0.10	Increases default risk

Interpretation:

• Credit Score (600) increased default risk by 20%.
• Income decreased risk by 15%.
• Age increased risk by 10%.
• The final probability is 0.65 based on these contributions.

🔹 SHAP gives local explainability for this specific person’s prediction, while IV only provides global feature importance.

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🚀 Key Takeaways

Feature	Information Value (IV)	SHAP Values
Measures	Overall feature importance	Individual prediction contribution
Scope	Global (across dataset)	Local + Global
Mathematics	Weight of Evidence (WOE)	Shapley Values (Game Theory)
Use Cases	Feature selection, credit scoring	Explaining model decisions, fairness auditing
Models Supported	Logistic Regression, Scorecards	Any ML model (XGBoost, Deep Learning, etc.)

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🎯 When to Use Which?

✅ Use IV when:

• You are selecting features for a classification model.
• You need to evaluate predictive power globally.

✅ Use SHAP when:

• You need model interpretability (why a model made a specific decision).
• You are working with complex models like XGBoost, Random Forests, Deep Learning.
• You need both local and global importance explanations.

pyspark code to get estimated size of dataframe in bytes

 from pyspark.sql import SparkSession

import sys

# Initialize a Spark session

spark = SparkSession.builder.appName("DataFrameSize").getOrCreate()

# Create a PySpark DataFrame

data = [(1, "John"), (2, "Alice"), (3, "Bob")]

columns = ["id", "name"]

df = spark.createDataFrame(data, columns)

# Get the size of the DataFrame in bytes

size_in_bytes = df.rdd.flatMap(lambda x: x).map(lambda x: sys.getsizeof(x) if x is not None else 0).sum()

print(f"Size of the DataFrame: {size_in_bytes} bytes")

# Stop the Spark session

spark.stop()

replaceWhere

If we want to replace content of table or file in a path below can be possible.

• The replaceWhere option atomically replaces all records that match a given predicate.

• You can replace directories of data based on how tables are partitioned using dynamic partition overwrites.

Python:

replace_data.write
  .mode("overwrite")
  .option("replaceWhere", "start_date >= '2017-01-02' AND end_date <= '2017-01-30'")
  .save("/tmp1/delta/events")

SQL:

INSERT INTO TABLE events REPLACE WHERE start_data >= '2017-01-01' AND end_date <= '2017-01-31' SELECT * FROM replace_data

Magic commands

 

Following are possible magic commands available in Databricks

1. %python
2. %sql
3. %scala
4. %sh
5. %fs → Alternatively, one can use dbutils.fs
6. %md

Note:

• The first line in the cell must be the magic command
• One cell allows only one magic command
• Magic commands are case sensitive
• When you change the default language, all cells in that notebook automatically add a magic command of the previous default language.

Tensorflow general methods

 #method to get shape of tensor flow element

#method to apply a method to all elements

#defining variables, constants in Tensorflow for a matrix of elements.

# Casting, activation functions

Synchronously shuffle X,Y

    import numpy as np

    np.random.seed(seed) 

    m = X.shape[1]                  # number of training examples    

    permutation = list(np.random.permutation(m))

    shuffled_X = X[:, permutation]

    shuffled_Y = Y[:, permutation].reshape((1, m))

Dropout

DROPOUT is a widely used regularization technique that is specific to deep learning. 

It randomly shuts down some neurons in each iteration. 

At each iteration, you shut down (= set to zero) each neuron of a layer with probability 1−keep_prob or keep it with probability  keep_prob (50% here). 

The dropped neurons don't contribute to the training in both the forward and backward propagations of the iteration.

When you shut some neurons down, you actually modify your model. The idea behind drop-out is that at each iteration, you train a different model that uses only a subset of your neurons. With dropout, your neurons thus become less sensitive to the activation of one other specific neuron, because that other neuron might be shut down at any time.

• Dropout is a regularization technique.
• You only use dropout during training. Don't use dropout (randomly eliminate nodes) during test time.
• Apply dropout both during forward and backward propagation.
• During training time, divide each dropout layer by keep_prob to keep the same expected value for the activations. For example, if keep_prob is 0.5, then we will on average shut down half the nodes, so the output will be scaled by 0.5 since only the remaining half are contributing to the solution. Dividing by 0.5 is equivalent to multiplying by 2. Hence, the output now has the same expected value. You can check that this works even when keep_prob is other values than 0.5.

L2 Regulerization

 

m=  # of training examples

l= layer

k , j=shape of weight matrix 

python - Initialization of weights

 The main difference between Gaussian variable (numpy.random.randn()) and uniform random variable is the distribution of the generated random numbers:

• numpy.random.rand() produces numbers in a uniform distribution.
• and numpy.random.randn() produces numbers in a normal distribution.

When used for weight initialization, randn() helps most the weights to Avoid being close to the extremes, allocating most of them in the center of the range.

An intuitive way to see it is, for example, if you take the sigmoid() activation function.

You’ll remember that the slope near 0 or near 1 is extremely small, so the weights near those extremes will converge much more slowly to the solution, and having most of them near the center will speed the convergence.

Initialization of weights

 

• The weights 𝑊[𝑙]${\mathrm{<br>}}^{}$ should be initialized randomly to break symmetry.
• However, it's okay to initialize the biases 𝑏[𝑙]${\mathrm{<br>}}^{<br>}$ to zeros. Symmetry is still broken so long as 𝑊[𝑙]${\mathrm{<br>}}^{}$ is initialized randomly.
• Initializing weights to very large random values doesn't work well.
• Initializing with small random values should do better.

python code to plot cost

import matplotlib.pyplot as plt

%matplotlib inline 

def plot_costs(costs, learning_rate=0.0075):

    plt.plot(np.squeeze(costs))

    plt.ylabel('cost')

    plt.xlabel('iterations (per hundreds)')

    plt.title("Learning rate =" + str(learning_rate))

    plt.show()

#Assuming "costs" is a list of costs obtained during training iterations per hundred

#calling the method with some learning rate

plot_costs(costs, learning_rate)

output: