熱門 Python 機器學習面試問題和答案

機器學習 (ML) 是科技業最受歡迎的領域之一，鑑於其豐富的函式庫和易用性，熟練 Python 通常是先決條件。如果您正在準備該領域的面試，那麼精通理論概念和實際實現至關重要。以下是一些常見的 Python ML 面試問題和答案，可幫助您做好準備。

1. Python 中您最熟悉哪些預處理技術？

預處理技術對於為機器學習模型準備資料至關重要。一些最常見的技術包括：

• 歸一化：將特徵向量中的值調整到共同的尺度，而不扭曲值範圍的差異。
• 虛擬變數：使用 pandas 建立指示變數（0 或 1），用於顯示分​​類變數是否可以採用特定值。
• 檢查異常值：可以使用多種方法，包括單變量、多變量和 Minkowski 錯誤。

程式碼範例：

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)

2.什麼是暴力演算法？提供一個例子。

暴力演算法徹底嘗試所有可能性來找到解決方案。一個常見的例子是線性搜索，其中演算法檢查數組的每個元素以查找匹配項。

程式碼範例：

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. 處理不平衡資料集的方法有哪些？

不平衡的資料集類別比例有偏差。處理此問題的策略包括：

• 收集更多數據：為少數群體收集更多數據。
• 重新取樣：對少數類進行過採樣或對多數類進行欠採樣。
• SMOTE（合成少數過採樣技術）：為少數類別產生合成樣本。
• 演算法調整：使用可以處理不平衡的演算法，例如 bagging 或 boosting 方法。

程式碼範例：

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.Python中有哪些處理缺失資料的方法？

處理缺失資料的常見策略包括省略和插補：

• 遺漏：刪除缺失值的行或欄位。
• 插補：使用平均值、中位數、眾數等技術或 SimpleImputer 或 IterativeImputer 等高階方法填入缺失值。

程式碼範例：

from sklearn.impute import SimpleImputer

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

5.什麼是回歸？如何在 Python 中實現迴歸？

迴歸是一種監督學習技術，用於尋找變數之間的相關性並對因變數進行預測。常見的例子包括線性迴歸和邏輯迴歸，可以使用 Scikit-learn 來實現。

程式碼範例：

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.如何在Python中分割訓練和測試資料集？

在 Python 中，您可以使用 Scikit-learn 中的 train_test_split 函數將資料拆分為訓練集和測試集。

程式碼範例：

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. 對於基於樹的學習器來說哪些參數最重要？

基於樹的學習器的一些關鍵參數包括：

• max_深度：每棵樹的最大深度。
• learning_rate：每次迭代的步長。
• n_estim- **n_estimators：整合中的樹數或 boosting 輪數。
• 子樣本：每棵樹要取樣的觀測值的分數。

程式碼範例：

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-learn 中常見的超參數調優方法有哪些？

兩種常用的超參數調整方法是：

• 網格搜尋：定義超參數值網格並蒐索最佳組合。
• 隨機搜尋：使用廣泛的超參數值並隨機迭代組合。

程式碼範例：

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. 寫一個函數來找出下雨天的降雨量中位數。

您需要刪除沒有下雨的日子，然後找到中位數。

程式碼範例：

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. 寫一個函數來估算所選加州起司的中位數價格來取代缺失值。

您可以使用 pandas 來計算並填入中位數。

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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