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。
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：
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)
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。
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)
不平衡的資料集類別比例有偏差。處理此問題的策略包括：
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)
處理缺失資料的常見策略包括省略和插補：
from sklearn.impute import SimpleImputer # Imputing missing values imputer = SimpleImputer(strategy='median') data_imputed = imputer.fit_transform(data)
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。
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)
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。
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)
基於樹的學習器的一些關鍵參數包括：
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)
兩種常用的超參數調整方法是：
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)
您需要刪除沒有下雨的日子，然後找到中位數。
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
您可以使用 pandas 來計算並填入中位數。
def impute_median_price(df, column):
median_price = df[column].median()
df[column].fillna(median_price, inplace=True)
return df
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
def grades_colors(df_students):
filtered_df = df_students[(df_students["grade"] > 90) & (df_students["favorite_color"].isin(["green", "red"]))]
return filtered_df
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)
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)
Note: This example uses simplified assumptions to meet the interview constraints.
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)
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)
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)
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.
"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."
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.
"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."
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.
"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."
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.
"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."
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.
"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."
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.
"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."
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.
"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."
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.
"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."
from sklearn.linear_model import Ridge # Applying L2 Regularization (Ridge Regression) ridge_model = Ridge(alpha=1.0) ridge_model.fit(X_train, y_train)
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.
"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."
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.
"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."
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)
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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