Pinterest OA – Implement Naive Bayes Classifier (Machine Learning Coding Assessment)

Question 1

You are asked to implement parts of a Naive Bayes classification algorithm from scratch, without using any libraries except numpy and math. Naive Bayes classification is based on Bayes’Theorem, predicting the probability that a set of data points belongs to one of several classes.

A full Naive Bayes classifier consists of four major steps:

1. Calculate feature statistics by class
   Compute the mean and variance of each feature in x_train for each class label in y_train.
2. Calculate prior probabilities
   Calculate the proportion of training samples belonging to each class.
3. Implement the Gaussian Density Function The density function is: f(x) = 1/(σ√(2π)) * exp(- (x - μ)² / (2σ²) ) where μ is the mean and σ is the standard deviation of the feature.

• Calculate posterior probabilities and make predictions
  Using Bayes’Theorem: ŷ = argmax_k P(C_k) * Π f(xi | C_k) where C_k is a class label.

You are given:

• x_train: 2D array of float values
• y_train: 1D array of class labels
• x_test: 2D array of float values (same format as train data, without labels)

Your task is to implement only the missing sections under # implement this.

Input

x_train = [[-2.6, 1.9, 2.0, 1.0],[-2.8, 1.7, -1.2, 1.5],[2.0, -0.9, 0.3, 2.3],[-1.5, -0.1, -1.6, -1.1],[-1.0, -0.6, -1.2, -0.7],[-0.3, 1.2, 2.6, 0.2],[-1.8, -1.3, -0.1, -1.2],[0.2, 1.2, -0.6, -1.3],[-5.2, 3.0, 0.2, 2.2],[-0.8, -0.1, 1.5, -0.1],[-2.3, 3.0, 0.8, 0.7],[0.2, 3.0, 3.6, -0.9],[1.7, -0.8, -0.0, 2.0],[2.8, 0.8, 1.8, -0.7]] y_train = [1, 2, 0, 0, 0, 1, 0, 1, 2, 0, 2, 1, 0, 2] x_test = [[-0.1, 1.4, 0.4, -1.0],[-1.3, 0.2, -1.3, -0.8],[-1.1, 1.5, -2.3, -2.5]]

Output

solution(x_train, y_train, x_test) = [1, 0, 1]

Question 2

Implement the missing code, denoted by ellipses. You may not modify the pre-existing code.

Your task is to implement parts of the Bagging algorithm from scratch (i.e., without importing any external libraries besides those already imported). As a reminder, Bagging classification comprises three major steps:

1. Bootstrap the train samples for each base classifier.
2. Train each classifier based on its own bootstrapped samples.
3. Assign class labels by a majority vote of the base classifiers.

To validate your implementation, you will use it for some classification tasks. Specifically, you will be given:

• x_train: a two-dimensional array of float values, where each subarray x_train[i] represents a unique case.
• y_train: a one-dimensional array where each element is the true class label of the corresponding x_train[i].
• x_test: test data in the same format as the training data but without labels.
• n_estimators: the number of base classifiers.

Your model should fit the training data, then classify each sample in x_test, and return the predicted class labels.

Notes

• All training/test data will be float values.
• Do not modify any pre-existing code—only fill in the # implement this parts.
• In a tie, choose the class label with the smaller number.
• Bootstrapping rules:
  • Generate indices using randint(0, n) where n is the number of training samples.
  • Indices may repeat by nature of bootstrapping.
  • seed is used only initially.

Question 3

You are given a string expr representing a sum of two positive integers. Both integers do not contain zeros in their decimal representation. For example, expr can contain "741+12" — but it cannot be equal to "74112" or "740+12".

You must add exactly one pair of parentheses to expr so that:

1. The plus sign '+' is inside the parentheses.
2. There is at least one digit before the plus sign and at least one digit after it.

For example:
"741+12" may be transformed into:

• "7(41+1)2"
• "74(1+12)"

But it cannot be turned into:

• "(74)1+12"
• "74(1)+12"

The resulting string must still represent a valid arithmetic expression.

You must compute the minimum possible value obtained after placing the parentheses.

Example:

For expr = "112+422",
all possible evaluated cases include:

(112+422) = 534  (11(2+4)22) = 308  (112+4)22 = 2552  1(12+422) = 434  1(12+4)22 = 108  ← smallest  ...

The output should be:

solution(expr) = 108

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