Python Prowess

Python Prowess

University

8 Qs

quiz-placeholder

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Python Prowess

Python Prowess

Assessment

Quiz

Other

University

Practice Problem

Medium

Created by

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8 questions

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1.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

Which of the following statistical measures provides information about the central tendency of a dataset?

Median

Variance

Interquartile range

Standard deviation

2.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

Which of the following visualization techniques is commonly used to explore the relationship between two continuous variables?

Box plot

bar chart

Histogram

Scatter plot

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the term "outlier" typically refer to in data exploration?

The average value of a dataset.

The standard deviation of a dataset.

A data point that lies within the main cluster of the dataset.

A data point that is significantly different from other observations in the dataset.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following best describes a decision tree?

A linear model used for regression analysis.

A clustering algorithm for grouping similar data points.

A graphical representation of decision-making based on probabilities.

A hierarchical structure composed of nodes and branches, representing decisions and outcomes.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What distinguishes Random Forest from a single Decision Tree?

Random Forest applies a different splitting criterion.

Random Forest uses a gradient boosting technique.

Random Forest requires fewer computational resources.

Random Forest combines the predictions of multiple decision trees.

6.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

___________  is the process of transforming raw data into features that are suitable for machine learning algorithms.

Transfer

Feature design

Transfer design

Feature engineering

7.

MULTIPLE CHOICE QUESTION

20 sec • 1 pt

What is the reason for overfitting or underfitting?

Loss is high

Accuracy is low

Training data is not cleaned

Model capacity is too small or too big

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