최고의 Python 기계 학습 인터뷰 질문 및 답변

기계 학습(ML)은 기술 업계에서 가장 인기 있는 분야 중 하나이며, 광범위한 라이브러리와 사용 편의성을 고려할 때 Python에 대한 능숙도가 전제 조건인 경우가 많습니다. 이 분야의 인터뷰를 준비하고 있다면 이론적 개념과 실제 구현 모두에 정통한 것이 중요합니다. 다음은 준비에 도움이 되는 몇 가지 일반적인 Python ML 인터뷰 질문과 답변입니다.

1. Python에서 가장 익숙한 전처리 기술은 무엇입니까?

전처리 기술은 기계 학습 모델용 데이터를 준비하는 데 필수적입니다. 가장 일반적인 기술은 다음과 같습니다.

• 정규화: 값 범위의 차이를 왜곡하지 않고 특징 벡터의 값을 공통 척도로 조정합니다.
• 가짜 변수: Pandas를 사용하여 범주형 변수가 특정 값을 가질 수 있는지 여부를 표시하는 표시 변수(0 또는 1)를 만듭니다.
• 이상값 확인: 일변량, 다변량 및 Minkowski 오류를 포함한 여러 가지 방법을 사용할 수 있습니다.

코드 예:

from sklearn.preprocessing import MinMaxScaler
import pandas as pd

# Data normalization
scaler = MinMaxScaler()
normalized_data = scaler.fit_transform(data)

# Creating dummy variables
df_with_dummies = pd.get_dummies(data, drop_first=True)

2. 무차별 대입 알고리즘이란 무엇입니까? 예시를 제공하세요.

무차별 알고리즘은 해결책을 찾기 위해 모든 가능성을 철저히 시도합니다. 일반적인 예는 알고리즘이 배열의 각 요소를 확인하여 일치 항목을 찾는 선형 검색입니다.

코드 예:

def linear_search(arr, target):
    for i in range(len(arr)):
        if arr[i] == target:
            return i
    return -1

# Example usage
arr = [2, 3, 4, 10, 40]
target = 10
result = linear_search(arr, target)

3. 불균형한 데이터 세트를 처리하는 방법은 무엇입니까?

불균형한 데이터세트로 인해 클래스 비율이 왜곡되었습니다. 이를 처리하기 위한 전략은 다음과 같습니다.

• 더 많은 데이터 수집: 소수계층을 위한 더 많은 데이터를 수집합니다.
• 리샘플링: 소수 클래스를 오버샘플링하거나 다수 클래스를 과소샘플링합니다.
• SMOTE(Synthetic Minority Oversampling Technique): 소수 클래스에 대한 합성 샘플을 생성합니다.
• 알고리즘 조정: 배깅이나 부스팅 방법과 같이 불균형을 처리할 수 있는 알고리즘을 사용합니다.

코드 예:

from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split

X_resampled, y_resampled = SMOTE().fit_resample(X, y)
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2)

4. Python에서 누락된 데이터를 처리하는 방법은 무엇입니까?

누락된 데이터를 처리하는 일반적인 전략에는 누락 및 대치가 있습니다.

• 생략: 누락된 값이 있는 행이나 열을 제거합니다.
• 대치: 평균, 중앙값, 모드와 같은 기술이나 SimpleImputer 또는 IterativeImputer와 같은 고급 방법을 사용하여 누락된 값을 채웁니다.

코드 예:

from sklearn.impute import SimpleImputer

# Imputing missing values
imputer = SimpleImputer(strategy='median')
data_imputed = imputer.fit_transform(data)

5. 회귀란 무엇입니까? Python에서 회귀를 어떻게 구현하시겠습니까?

회귀는 변수 간의 상관관계를 찾고 종속 변수를 예측하는 데 사용되는 지도 학습 기술입니다. 일반적인 예로는 Scikit-learn을 사용하여 구현할 수 있는 선형 회귀 및 로지스틱 회귀가 있습니다.

코드 예:

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

6. Python에서 교육 및 테스트 데이터 세트를 어떻게 분할합니까?

Python에서는 Scikit-learn의 train_test_split 함수를 사용하여 데이터를 훈련 세트와 테스트 세트로 분할할 수 있습니다.

코드 예:

from sklearn.model_selection import train_test_split

# Split the dataset: 60% training and 40% testing
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.4)

7. 트리 기반 학습자에게 가장 중요한 매개변수는 무엇입니까?

트리 기반 학습자를 위한 몇 가지 중요한 매개변수는 다음과 같습니다.

• max_length: 트리당 최대 깊이
• learning_rate: 각 반복의 단계 크기
• n_estim- **n_estimators: 앙상블의 트리 수 또는 부스팅 라운드 수.
• subsample: 각 트리에 대해 샘플링할 관측치의 비율입니다.

코드 예:

from sklearn.ensemble import RandomForestClassifier

# Setting parameters for Random Forest
model = RandomForestClassifier(max_depth=5, n_estimators=100, max_features='sqrt', random_state=42)
model.fit(X_train, y_train)

8. Scikit-learn의 일반적인 하이퍼파라미터 조정 방법은 무엇입니까?

초매개변수 조정을 위한 두 가지 일반적인 방법은 다음과 같습니다.

• 그리드 검색: 하이퍼파라미터 값의 그리드를 정의하고 최적의 조합을 검색합니다.
• 무작위 검색: 광범위한 하이퍼매개변수 값을 사용하고 조합을 통해 무작위로 반복합니다.

코드 예:

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV

# Grid Search
param_grid = {'n_estimators': [50, 100, 200], 'max_depth': [5, 10, 15]}
grid_search = GridSearchCV(model, param_grid, cv=5)
grid_search.fit(X_train, y_train)

# Random Search
param_dist = {'n_estimators': [50, 100, 200], 'max_depth': [5, 10, 15]}
random_search = RandomizedSearchCV(model, param_dist, n_iter=10, cv=5, random_state=42)
random_search.fit(X_train, y_train)

9. 비가 내린 날의 평균 강우량을 구하는 함수를 작성하세요.

비가 오지 않는 날을 빼고 중앙값을 구해야 합니다.

코드 예:

def median_rainfall(df_rain):
    # Remove days with no rain
    df_rain_filtered = df_rain[df_rain['rainfall'] > 0]
    # Find the median amount of rainfall
    median_rainfall = df_rain_filtered['rainfall'].median()
    return median_rainfall

10. 누락된 값 대신 선택한 캘리포니아 치즈의 중간 가격을 대치하는 함수를 작성하세요.

Pandas를 사용하여 중앙값을 계산하고 채울 수 있습니다.

Code Example:

def impute_median_price(df, column):
    median_price = df[column].median()
    df[column].fillna(median_price, inplace=True)
    return df

11. Write a Function to Return a New List Where All None Values Are Replaced with the Most Recent Non-None Value in the List.

Code Example:

def fill_none(input_list):
    prev_value = None
    result = []
    for value in input_list:
        if value is None:
            result.append(prev_value)
        else:
            result.append(value)
            prev_value = value
    return result

12. Write a Function Named grades_colors to Select Only the Rows Where the Student’s Favorite Color is Green or Red and Their Grade is Above 90.

Code Example:

def grades_colors(df_students):
    filtered_df = df_students[(df_students["grade"] > 90) & (df_students["favorite_color"].isin(["green", "red"]))]
    return filtered_df

13. Calculate the t-value for the Mean of ‘var’ Against a Null Hypothesis That μ = μ_0.

Code Example:

import pandas as pd
from scipy import stats

def calculate_t_value(df, column, mu_0):
    sample_mean = df[column].mean()
    sample_std = df[column].std()
    n = len(df)

    t_value = (sample_mean - mu_0) / (sample_std / (n ** 0.5))
    return t_value

# Example usage
t_value = calculate_t_value(df, 'var', mu_0)
print(t_value)

14. Build a K-Nearest Neighbors Classification Model from Scratch.

Code Example:

import numpy as np
import pandas as pd

def euclidean_distance(point1, point2):
    return np.sqrt(np.sum((point1 - point2) ** 2))

def kNN(k, data, new_point):
    distances = data.apply(lambda row: euclidean_distance(row[:-1], new_point), axis=1)
    sorted_indices = distances.sort_values().index
    top_k = data.iloc[sorted_indices[:k]]

    return top_k['label'].mode()[0]

# Example usage
data = pd.DataFrame({
    'feature1': [1, 2, 3, 4],
    'feature2': [2, 3, 4, 5],
    'label': [0, 0, 1, 1]
})

new_point = [2.5, 3.5]
k = 3

result = kNN(k, data, new_point)
print(result)

15. Build a Random Forest Model from Scratch.

Note: This example uses simplified assumptions to meet the interview constraints.

Code Example:

import pandas as pd
import numpy as np

def create_tree(dataframe, new_point):
    unique_classes = dataframe['class'].unique()
    for col in dataframe.columns[:-1]:  # Exclude the 'class' column
        if new_point[col] == 1:
            sub_data = dataframe[dataframe[col] == 1]
            if len(sub_data) > 0:
                return sub_data['class'].mode()[0]
    return unique_classes[0]  # Default to the most frequent class

def random_forest(df, new_point, n_trees):
    results = []
    for _ in range
n_trees):
        tree_result = create_tree(df, new_point)
        results.append(tree_result)
    # Majority vote
    return max(set(results), key=results.count)

# Example usage
df = pd.DataFrame({
    'feature1': [0, 1, 1, 0],
    'feature2': [0, 0, 1, 1],
    'class': [0, 1, 1, 0]
})

new_point = {'feature1': 1, 'feature2': 0}
n_trees = 5

result = random_forest(df, new_point, n_trees)
print(result)

16. Build a Logistic Regression Model from Scratch.

Code Example:

import pandas as pd
import numpy as np

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

def logistic_regression(X, y, num_iterations, learning_rate):
    weights = np.zeros(X.shape[1])
    for i in range(num_iterations):
        z = np.dot(X, weights)
        predictions = sigmoid(z)
        errors = y - predictions
        gradient = np.dot(X.T, errors)

gradient = np.dot(X.T, errors)
        weights += learning_rate * gradient
    return weights

# Example usage
df = pd.DataFrame({
    'feature1': [0, 1, 1, 0],
    'feature2': [0, 0, 1, 1],
    'class': [0, 1, 1, 0]
})

X = df[['feature1', 'feature2']].values
y = df['class'].values
num_iterations = 1000
learning_rate = 0.01

weights = logistic_regression(X, y, num_iterations, learning_rate)
print(weights)

17. Build a K-Means Algorithm from Scratch.

Code Example:

import numpy as np

def k_means(data_points, k, initial_centroids):
    centroids = initial_centroids
    while True:
        distances = np.linalg.norm(data_points[:, np.newaxis] - centroids, axis=2)
        clusters = np.argmin(distances, axis=1)
        new_centroids = np.array([data_points[clusters == i].mean(axis=0) for i in range(k)])        

        if np.all(centroids == new_centroids):
            break
        centroids = new_centroids
    return clusters

# Example usage
data_points = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])
k = 2
initial_centroids = np.array([[1, 2], [10, 2]])

clusters = k_means(data_points, k, initial_centroids)
print(clusters)

18. What is Machine Learning and How Does it Work?

Machine Learning is a field of artificial intelligence focused on building algorithms that enable computers to learn from data without explicit programming. It uses algorithms to analyze and identify patterns in data and make predictions based on those patterns.

Example Answer:

"Machine learning is a branch of artificial intelligence that involves creating algorithms capable of learning from and making predictions based on data. It works by training a model on a dataset and then using that model to make predictions on new data."

19. What are the Different Types of Machine Learning Algorithms?

There are three main types of machine learning algorithms:

• Supervised Learning: Useslabeled data and makes predictions based on this information. Examples include linear regression and classification algorithms.

• Unsupervised Learning: Processes unlabeled data and seeks to find patterns or relationships in it. Examples include clustering algorithms like K-means.

• Reinforcement Learning: The algorithm learns from interacting with its environment, receiving rewards or punishments for certain actions. Examples include training AI agents in games.

Example Answer:

"There are three main types of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled data to make predictions, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns from interactions with the environment to maximize rewards."

20. What is Cross-Validation and Why is it Important in Machine Learning?

Cross-validation is a technique to evaluate the performance of a machine learning model by dividing the dataset into two parts: a training set and a validation set. The training set trains the model, whereas the validation set evaluates it.

Importance:

• Prevents overfitting by ensuring the model generalizes well to unseen data.
• Provides a more accurate measure of model performance.

Example Answer:

"Cross-validation is a technique used to evaluate a machine learning model'sperformance by dividing the dataset into training and validation sets. It helps ensure the model generalizes well to new data, preventing overfitting and providing a more accurate measure of performance."

21. What is an Artificial Neural Network and How Does it Work?

Artificial Neural Networks (ANNs) are models inspired by the human brain's structure. They consist of layers of interconnected nodes (neurons) that process input data and generate output predictions.

Example Answer:

"An artificial neural network is a machine learning model inspired by the structure and function of the human brain. It comprises layers of interconnected neurons that process input data through weighted connections to make predictions."

22. What is a Decision Tree and How to Use it in Machine Learning?

Decision Trees are models for classification and regression tasks that split data into subsets based on the values of input variables to generate prediction rules.

Example Answer:

"A decision tree is a tree-like model used for classification and regression tasks. It works by recursively splitting data into subsets based on input variables, creating rules for making predictions."

23. What is the K-Nearest Neighbors (KNN) Algorithm and How Does it Work?

K-Nearest Neighbors (KNN) is a simple machine learning algorithm usedfor classification or regression tasks. It determines the k closest data points in the feature space to a given unseen data point and classifies it based on the majority class of its k nearest neighbors.

Example Answer:

"The K-Nearest Neighbors (KNN) algorithm is a machine learning technique used for classification or regression. It works by identifying the k closest data points to a given point in the feature space and classifying it based on the majority class among the k nearest neighbors."

24. What is the Support Vector Machine Algorithm and How Does it Work?

Support Vector Machines (SVM) are linear models used for binary classification and regression tasks. They find the most suitable boundary (hyperplane) that separates data into classes. Data points closest to the hyperplane, called support vectors, play a critical role in defining this boundary.

Example Answer:

"The Support Vector Machine (SVM) algorithm is a linear model used for binary classification and regression tasks. It identifies the best hyperplane that separates data into classes, relying heavily on the data points closest to the hyperplane, known as support vectors."

25. What is Regularization, and How Do You Use it in Machine Learning?

Regularization is a technique to prevent overfitting in machinelearning models by adding a penalty term to the loss function. This penalty discourages the model from learning overly complex relationships in the data.

Example Answer:

"Regularization is a technique to prevent overfitting in machine learning models by adding a penalty term to the loss function, which discourages the model from learning overly complex patterns. Common types of regularization include L1 (Lasso) and L2 (Ridge) regularization."

Code Example:

from sklearn.linear_model import Ridge

# Applying L2 Regularization (Ridge Regression)
ridge_model = Ridge(alpha=1.0)
ridge_model.fit(X_train, y_train)

26. Can You Explain How Gradient Descent Works?

Gradient Descent is an optimization algorithm used to minimize a cost function in machine learning. It iteratively adjusts the parameters of the model in the direction of the negative gradient of the cost function until it reaches a minimum.

Example Answer:

"Gradient Descent is an optimization algorithm used to minimize a cost function in machine learning. It iteratively updates the model parameters in the direction of the negative gradient of the cost function, aiming to find the parameters that minimize the cost."

27. Can You Explain the Concept of Ensemble Learning

Ensemble Learning is a technique where multiple models (often called "weak learners") are combined to solve a prediction task. The combined model is generally more robust and performs better than individual models.

Example Answer:

"Ensemble learning is a machine learning technique where multiple models are combined to solve a prediction task. Common ensemble methods include bagging, boosting, and stacking. Combining the predictions of individual models can improve performance and reduce the risk of overfitting."

Example Code for Random Forest (an ensemble method):

from sklearn.ensemble import RandomForestClassifier

# Ensemble learning using Random Forest
model = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Conclusion

Preparing for a Python machine learning interview involves understanding both theoretical concepts and practical implementations. This guide has covered several essential questions and answers that frequently come up in interviews. By familiarizing yourself with these topics and practicing the provided code examples, you'll be well-equipped to handle a wide range of questions in your next machine learning interview. Good luck!

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