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.
전처리 기술은 기계 학습 모델용 데이터를 준비하는 데 필수적입니다. 가장 일반적인 기술은 다음과 같습니다.
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)
무차별 알고리즘은 해결책을 찾기 위해 모든 가능성을 철저히 시도합니다. 일반적인 예는 알고리즘이 배열의 각 요소를 확인하여 일치 항목을 찾는 선형 검색입니다.
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)
불균형한 데이터세트로 인해 클래스 비율이 왜곡되었습니다. 이를 처리하기 위한 전략은 다음과 같습니다.
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)
누락된 데이터를 처리하는 일반적인 전략에는 누락 및 대치가 있습니다.
from sklearn.impute import SimpleImputer # Imputing missing values imputer = SimpleImputer(strategy='median') data_imputed = imputer.fit_transform(data)
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.
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)
Python에서는 Scikit-learn의 train_test_split 함수를 사용하여 데이터를 훈련 세트와 테스트 세트로 분할할 수 있습니다.
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)
트리 기반 학습자를 위한 몇 가지 중요한 매개변수는 다음과 같습니다.
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)
초매개변수 조정을 위한 두 가지 일반적인 방법은 다음과 같습니다.
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)
비가 오지 않는 날을 빼고 중앙값을 구해야 합니다.
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
Pandas를 사용하여 중앙값을 계산하고 채울 수 있습니다.
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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