QMB 3302 Final Verified A+
NLP stands for ✔️✔️Natural Language Processing
Tokenization, as defined in the lecture is... ✔️✔️a computer turning letters and/or words into
something it can read and understand, like numbers
Recommenders come in many flavors. 2 of the most common, often used together and discussed in the
lecture are: ✔️✔️1) Item Based
2) User Based
Imagine you have a dataset with 2 columns, both filled with continuous numbers. You believe the first
column is a predictor of the second column. Which of the model approaches could work when building a
model? ✔️✔️Random Forest
Decision Trees
Regression
Decision trees have a few problems, you should probably review those for the final exam! The problem
we talked about the most is: ✔️✔️Overfitting
A straight line fit is the model of the form y = ax + b, where a is commonly known as the _____, and b is
commonly known as the _____ ✔️✔️slope, intercept
The LinearRegression estimator is only capable of simple straight line fits. ✔️✔️False
What are the 5 steps to building a machine learning model? ✔️✔️1. Choose a class of model
2. Choose hyperparameters
3. Arrange data
4. Fit the model
, 5. Predict
What is the purpose of the below code?
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np ✔️✔️Import python packages
Choosing a class of model.
Your dataset consists of details about customer traits, such as "number of items in basket at checkout"
and "time of day at checkout". Your task is to group customers that are like each other together. You
don't already have labeled customer types. What kind of model are you building? ✔️✔️Unsupervised
model (such as K means)
What is ONE reason the textbook lists for why a Linear regression is a good starting point in a modeling
task. ✔️✔️They are interpretable
What type of classifier is a random forest? ✔️✔️Non-parametric
What are random forests an example of? ✔️✔️Ensemble method
What does a decision tree do? ✔️✔️Classify objects by asking a series of questions
How does a well-constructed decision tree operate? ✔️✔️Each question cuts the number of options
by approximately half
Pipelines are useful (in the analytics with Python sense) for the following reasons? ✔️✔️Pipelines
make it easy to repeat/replicate steps and run multiple models;
Pipelines help organize the code you used to clean and treat your data;
NLP stands for ✔️✔️Natural Language Processing
Tokenization, as defined in the lecture is... ✔️✔️a computer turning letters and/or words into
something it can read and understand, like numbers
Recommenders come in many flavors. 2 of the most common, often used together and discussed in the
lecture are: ✔️✔️1) Item Based
2) User Based
Imagine you have a dataset with 2 columns, both filled with continuous numbers. You believe the first
column is a predictor of the second column. Which of the model approaches could work when building a
model? ✔️✔️Random Forest
Decision Trees
Regression
Decision trees have a few problems, you should probably review those for the final exam! The problem
we talked about the most is: ✔️✔️Overfitting
A straight line fit is the model of the form y = ax + b, where a is commonly known as the _____, and b is
commonly known as the _____ ✔️✔️slope, intercept
The LinearRegression estimator is only capable of simple straight line fits. ✔️✔️False
What are the 5 steps to building a machine learning model? ✔️✔️1. Choose a class of model
2. Choose hyperparameters
3. Arrange data
4. Fit the model
, 5. Predict
What is the purpose of the below code?
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np ✔️✔️Import python packages
Choosing a class of model.
Your dataset consists of details about customer traits, such as "number of items in basket at checkout"
and "time of day at checkout". Your task is to group customers that are like each other together. You
don't already have labeled customer types. What kind of model are you building? ✔️✔️Unsupervised
model (such as K means)
What is ONE reason the textbook lists for why a Linear regression is a good starting point in a modeling
task. ✔️✔️They are interpretable
What type of classifier is a random forest? ✔️✔️Non-parametric
What are random forests an example of? ✔️✔️Ensemble method
What does a decision tree do? ✔️✔️Classify objects by asking a series of questions
How does a well-constructed decision tree operate? ✔️✔️Each question cuts the number of options
by approximately half
Pipelines are useful (in the analytics with Python sense) for the following reasons? ✔️✔️Pipelines
make it easy to repeat/replicate steps and run multiple models;
Pipelines help organize the code you used to clean and treat your data;
