WorksheetsErode Quiz ML
Total questions: 20
Worksheet time: 16mins
(a) is an ensemble learning technique that improves the performance of a machine learning model by sequentially training weak models, with each new model focusing on correcting the errors made by the previous models.
If we didn't assign a base estimators to the bagging classifier it will use by default:
Linear regression
Decision tree
KNN
Logistic regression
What is meant by the number of estimators:
The number of base estimators in the ensemble.
The number of features
The number of accuracies
The number of data rows
A value that defines the step taken at each iteration, before correction?
Gradient descent
learning rate
l2 regularization
l1 regularization
Which of these is not a supervised learning algorithm?
Representation Learning
Classification
Regression
Which of the following methods do we use to find the best fit line for data in Linear Regression?
Least Square Error
Maximum Likelihood
Logarithmic Loss
Both A and B
_____ is a learning model that is used to identify a relationship between large amounts of information from a data set.
Classification
Multi class classification
Association
Unsupervised learning
The goal of clustering a set of data is to?
choose the best data from the set
divide them into groups of data that are near each other
determine the nearest neighbors of each of the data
predict the class of data
Agglomerative hierarchical clustering is also known as
Top down approach
Bottom up approach
Both of these
None of these
In unsupervised learning, the machine learns by
Identifying patterns in the dataset
Using class labels
None of these
Both of these
What is the purpose of Principal Component Analysis (PCA)?
To increase the dimensionality of a dataset
To remove outliers from the dataset
To add noise to the dataset
To reduce the dimensionality of a dataset
What is the goal of dimensionality reduction in machine learning?
To reduce the efficiency of storage and computation
To simplify the dataset while retaining its essential characteristics
To increase the number of input variables in a dataset
To complicate the dataset while retaining its essential characteristics
Imagine, you have 1000 input features and 1 target feature in a machine learning problem. You have to select 100 most important features based on the relationship between input features and the target features.
Do you think, this is an example of dimensionality reduction?
Yes
No
I have 4 variables in the dataset such as - A, B, C & D. I have performed the following actions:
Step 1: Using the above variables, I have created two more variables, namely E = A + 3 B and F = B + 5 C + D.
Step 2: Then using only the variables E and F I have built a Random Forest model.
Could the steps performed above represent a dimensionality reduction method?
No
Yes
The linear model to represent the distance (in miles) traveled given the time (in hours) driving is
m=−1.79+61.93h . Predict how far a person will travel driving 10 hours.617.51 miles
621.09 miles
100 miles
0.19 miles
A study conducted to determine if one can predict the yield of a crop based on the amount of fertilizer applied to the soil. The response variable in this study is ...
yield of the crop
the experimeter
amount of fertilizer applied to the soil
the soil
