WorksheetsMachine Learning
Total questions: 20
Worksheet time: 15mins
Aarush is a data scientist who has developed a machine learning model to predict house prices based on various features. However, he notices that the model performs exceptionally well on the training data but fails to generalize to new data. What is this phenomenon called?
Fitting a model with insufficient data
Fitting a model too closely to the training data
Fitting a model with too few features
Fitting a model to the validation set
Aashi is developing a machine learning model to predict house prices. She wants to ensure that her model performs well on new, unseen data. What is the purpose of cross-validation in machine learning?
To evaluate the model's performance on unseen data
To increase the size of the training dataset
To reduce the complexity of the model
To optimize the model's hyperparameters
In a data science competition, a team of analysts is trying to improve their model's performance by selecting the most relevant features from their dataset. Which of the following is a common method for feature selection that they might use?
Gradient Descent
Principal Component Analysis (PCA)
Random Forest
Support Vector Machine
Mira is a data scientist working on a machine learning project. She is trying to improve her model's performance and is considering how to balance the complexity of her model. What does the term 'bias-variance tradeoff' refer to in this context?
The effect of regularization on model complexity
The impact of data size on model performance
The relationship between training and validation accuracy
The balance between underfitting and overfitting a model
In a recent project, Kavya developed a machine learning model to classify emails as spam or not spam. To assess how well her model was performing, she decided to use a confusion matrix. What is the purpose of using a confusion matrix in classification tasks?
To select features for the model
To visualize the distribution of the dataset
To evaluate the performance of a classification model
To optimize the model's parameters
In a tech company, a team of data scientists is working on improving the accuracy of their predictive models for customer behavior. They decide to use a method that involves combining the predictions from several different models to achieve better results. What does this method refer to?
Reducing the number of features in a dataset
Training models on different datasets
Using a single model for predictions
Combining multiple models to improve performance
Riyaan is analyzing a dataset of customer preferences to group similar customers together for targeted marketing. Which algorithm is commonly used for clustering tasks?
Linear Regression
k-Means
Decision Trees
Logistic Regression
Neha is working on a machine learning project and needs to improve her model's performance. She realizes that she needs to optimize the settings that govern the training process to achieve better results. What does the term 'hyperparameter tuning' refer to?
Evaluating the model on a test dataset
Adjusting the model's parameters during training
Choosing the best model architecture
Optimizing the settings that govern the training process
Myra is developing a machine learning model to predict housing prices. She notices that her model is becoming too complex and is overfitting the training data. What is the purpose of using regularization in her model?
To improve the model's interpretability
To enhance the model's accuracy on the training set
To increase the training speed
To prevent the model from becoming too complex
Asher is a data scientist working on a project that involves predicting customer behavior. He is considering different machine learning models for his analysis. What is the main advantage of using decision trees in machine learning?
They can handle both numerical and categorical data
They require less data preprocessing
They are less interpretable than other models
They are always more accurate than other models
Riyaan is training a machine learning model to predict house prices. He is trying to understand how quickly his model learns from the data he provides. What does the term 'learning rate' refer to in the context of training machine learning models?
The number of iterations in training
The speed at which a model learns from data
The complexity of the model architecture
The size of the training dataset
Kavya is training a neural network for a project, but she notices that the model performs well on the training data but poorly on the validation data. To improve her model's performance and prevent overfitting, which of the following techniques should she consider?
Reducing the learning rate
Increasing the number of layers
Using dropout layers
Using more training data
Identify the Problem:
We have Information of all previous general elections with respect to party, votes, Candidates, Locality, Issues in nation, etc. With All this information we need to find the winner of next general election.
Classification
Regression
Clustering
none of them
Identify the problem:
Tara is a real estate analyst who has gathered information on all houses in a particular area, including size, type and design of house, locality, colour, previous purchase, etc. Based on this data, she needs to determine the price of a specific house.
Classification
Regression
Clustering
Prbabilistic
Identify a problem:
Riyaan has details of corona affected positive cases found in various regions. Depending upon parameters like number of increasing corona infected patients, weather conditions etc. regions are classified into red zone, Green zone and Orange Zone. On the basis of these data we have to predict the region of a city.
classification
regression
clustering
unsupervised learning
Cross validation is used for
Comparing predictors
Selecting parameters in prediction function
Selecting variables to include in a model
All of the mentioned
During a data science workshop, a group of participants is discussing various methods of cross-validation to evaluate their machine learning models. They come across several techniques and want to identify which one is not a recognized method. Which of the following is not a method of cross validation?
Leave One Out CV
K-Fold CV
Stratified K-Fold CV
Timeline CV
Riyaan is working on a project that involves categorizing different types of fruits based on their characteristics. He needs to choose a method for multiclass classification. Which is/are method to do multiclass classification?
One Vs Rest
One vs One
All vs One
One vs Other
In a recent data science project, Divya was tasked with predicting whether a customer would buy a product based on their online behavior. To ensure her model's output was a probability between 0 and 1, which of the following functions did she use?
Sigmoid
polynomial
square
rbf
In a machine learning competition, Naira is trying to build a model to predict house prices. She notices that her model performs exceptionally well on the training data but poorly on the validation data. Which of the following statements is True regarding her model's performance?
Model which overfit has high bias and high variance
Model which overfits have Low bias and low variance
Model which overfits has high Bias and Low variance
Model which overfits has low Bias and High Variance
