1. What is Scikit-learn?
A powerful Python library for machine learning. It provides tools for classification, regression, clustering, and model evaluation.
2. How to train a basic model in Scikit-learn?
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
3. How to make predictions?
predictions = model.predict(X_test)
4. What is train_test_split used for?
To split data into training and testing sets.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
5. How to evaluate model performance?
Use metrics like accuracy, precision, recall, F1-score, or RMSE.
from sklearn.metrics import accuracy_score
accuracy_score(y_test, predictions)
6. What is cross-validation?
A technique to assess model performance by splitting data into multiple folds.
from sklearn.model_selection import cross_val_score
cross_val_score(model, X, y, cv=5)
7. How to standardize features?
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
8. What is a pipeline in Scikit-learn?
A way to chain preprocessing and modeling steps.
from sklearn.pipeline import Pipeline
pipe = Pipeline([('scaler', StandardScaler()), ('model', LinearRegression())])
9. How to tune hyperparameters?
Use GridSearchCV or RandomizedSearchCV.
from sklearn.model_selection import GridSearchCV
grid = GridSearchCV(model, param_grid, cv=5)
🔟 What are common algorithms in Scikit-learn?
⦁ LinearRegression
⦁ LogisticRegression
⦁ DecisionTreeClassifier
⦁ RandomForestClassifier
⦁ KMeans
⦁ SVM
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