Home / Past papers / MLP Quiz 1 / this paper
2024 Oct27: - 27 Oct 2024
Elapsed 00:00
Comprehension passage - the questions below it refer to this
Based on the above data, answer the given subquestions.
Which option will give the total count of canceled flights in the dataset?
Which of the following code snippets will add a new column ’OnTime’ that shows True if the flight was not delayed, else False?
How can you filter the flights that have a delay due to ’Weather’?
Which option will sort the flights first by `Airline` in ascending order, then by DelayMinutes in descending order?
Which option will give the count of flights that have a delay greater than the average delay?
Comprehension passage - the questions below it refer to this
Based on the above data, answer the given subquestions.
- (4, 5)
- (4, 6)
- (4, 7)
- (4, 8)
- None of these
- 0.0
- 0.5
- -0.5
- 1
- None of these
Which of the following preprocessing techniques helps in handling categorical features in a dataset?
- One-hot encoding
- StandardScaler
- Label encoding
- MinMaxScaler
Which of the following imputation strategies can be used for handling missing values?
- Filling with mean or median
- Deleting the rows with missing values
- Using a predictive model to estimate missing values
- Using a random generator to fill missing values
Which of the following is correct about cross-validation in machine learning?
- Cross-validation helps prevent overfitting.
- It is a technique to improve the model’s accuracy.
- K-Fold cross-validation is one of the most popular types of cross-validation.
- Cross-validation is not suitable for small datasets.
Which of the following are valid loss functions for SGDClassifier?
- Squared Loss
- Hinge Loss
- Log Loss
- Mean Absolute Error
- 0 and 1
- 2 and 3
- 0 and 3
- 1 and 2
- 1 and 3
- 0
- 4
- 6
- None of these
How many different models will be trained during this grid search process?
- 36
- 54
- 72
- 108
You are working on a machine learning project and have received a dataset containing numeric and categorical features. The dataset has some missing values and potential outliers. Given the following data cleaning steps: 1. Use One-Hot Encoding for categorical variables. 2. Impute missing values with feature’s mean for numeric features. 3. Remove duplicates. 4. Standardize numeric features using Z-score normalization. 5. Identify and handle outliers using the IQR method. Which of the following represents the MOST appropriate sequence for preparing the data for a machine learning model?
- 1 → 4 → 2 → 3 → 5
- 3 → 2 → 1 → 4 → 5
- 2 → 3 → 1 → 5 → 4
- 3 → 5 → 2 → 1 → 4
- 2
- 3
- 1
- None of these
- It controls the learning rate of the stochastic regressor during training.
- It determines the maximum number of iterations for the training process.
- It defines the fraction of the validation set used for early stopping.
- It specifies the tolerance level for early stopping based on the change in thevalidation error.
- Intercept: 3.0, Coefficients: [1.0, 2.0]
- Intercept: 3.0, Coefficients: [0.0, 2.0]
- Intercept: 0.0, Coefficients: [1.0, 2.0]
- Given code will return an error.
- Controls the total number of iterations.
- Specifies the tolerance level for early stopping.
- Defines the number of iterations with no improvement to stop training.
- Sets the learning rate schedule.
Answer key
- Q1 (MCQ, 2 marks): A
- Q2 (MCQ, 2 marks): A
- Q3 (MSQ, 3 marks): B, C
- Q4 (MCQ, 3 marks): A
- Q5 (MCQ, 3 marks): A
- Q6 (MCQ, 2 marks): A
- Q7 (MCQ, 3 marks): D
- Q8 (MSQ, 2 marks): A, C
- Q9 (MSQ, 2 marks): A, B, C
- Q10 (MSQ, 2 marks): A, C
- Q11 (MSQ, 2 marks): B, C
- Q12 (MSQ, 2 marks): A, D
- Q13 (MCQ, 3 marks): B
- Q14 (MCQ, 3 marks): A
- Q15 (MCQ, 3 marks): A
- Q16 (MCQ, 2 marks): D
- Q17 (MCQ, 2 marks): A
- Q18 (MCQ, 2 marks): D
- Q19 (MCQ, 2 marks): D
- Q20 (MCQ, 2 marks): C
- Q21 (MCQ, 2 marks): A
- Q22 (MCQ, 1 marks): C
Concepts this paper tests
Auto-generated summary from the source site. Handy as a checklist; the lessons above explain each idea from scratch.
Summary of Core Topics, Concepts, Principles, and Equations
1. Pandas DataFrame Operations
- Counting and Summing:
df['Cancelled'].sum(),df['Cancelled'].count() - Filtering:
df[df['DelayReason'] == 'Weather'] - Sorting:
df.sort_values(by=['Airline', 'DelayMinutes'], ascending=[True, False]) - Mean and Median:
df['DelayMinutes'].mean(),df['DelayMinutes'].median()
2. Data Preprocessing
One-Hot Encoding: Converting categorical variables into a form that can be provided to machine learning algorithms.
from sklearn.preprocessing import OneHotEncoder encoder = OneHotEncoder() encoded_data = encoder.fit_transform(categorical_data)StandardScaler: Standardizing features by removing the mean and scaling to unit variance.
from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaled_data = scaler.fit_transform(data)MinMaxScaler: Scaling features to a given range.
from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() scaled_data = scaler.fit_transform(data)KNNImputer: Imputing missing values using k-nearest neighbors.
from sklearn.impute import KNNImputer imputer = KNNImputer(n_neighbors=2) imputed_data = imputer.fit_transform(data)
3. Pipeline and ColumnTransformer
Pipeline: Sequentially applying a list of transforms and a final estimator.
from sklearn.pipeline import Pipeline pipeline = Pipeline([('impute', KNNImputer(n_neighbors=2)), ('scale', MinMaxScaler())]) transformed_data = pipeline.fit_transform(data)ColumnTransformer: Applying different transforms to different columns.
from sklearn.compose import ColumnTransformer ct = ColumnTransformer([('num', pipeline, [0, 2]), ('cat', OneHotEncoder(), [1])]) transformed_data = ct.fit_transform(data)
4. Cross-Validation and Grid Search
StratifiedKFold: Providing train/test indices to split data in train/test sets.
from sklearn.model_selection import StratifiedKFold skf = StratifiedKFold(n_splits=2) for train_index, test_index in skf.split(X, y): print("TRAIN:", train_index, "TEST:", test_index)GridSearchCV: Exhaustive search over specified parameter values for an estimator.
from sklearn.model_selection import GridSearchCV grid = GridSearchCV(estimator=pipeline, param_grid=param_grid, scoring='r2', n_jobs=-1, cv=3) grid.fit(X, y)
5. Machine Learning Models
SGDRegressor: Linear model fitted by minimizing a regularized empirical loss with SGD.
from sklearn.linear_model import SGDRegressor model = SGDRegressor(max_iter=1000, tol=1e-3) model.fit(X, y)LinearRegression: Ordinary least squares Linear Regression.
from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X, y)
6. Mathematical Equations
- Mean:
- Standard Deviation:
- Manhattan Distance: