# Einführung Data Science at Universität Stuttgart

## Flashcards and summaries for Einführung Data Science at the Universität Stuttgart

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### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What is a qualitative variable?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What are discrete variables?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What is an Continuous variable?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What is the major goal of supervised learning?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What is meant by Classification?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What is the problem with hyperparameters?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What are approaches inherited from the hyperparameter optimization?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What fundamental problem lies ahead when searching for the right learning rate?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What does iterative optimization algorithm mean?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

To what refers Iterations?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What are the main problems of the Batch Gradient Descent?

### Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

What ensures strict convexity?

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## Exemplary flashcards for Einführung Data Science at the Universität Stuttgart on StudySmarter:

Einführung Data Science

What is a qualitative variable?
Also called categorical variables, take on values that are names or labels e.g. the gender

Einführung Data Science

What are discrete variables?
Can only take on a finite number of values, eg. The number of cars owned.

Einführung Data Science

What is an Continuous variable?
Can take on any value in a certain range, eg. The body height

Einführung Data Science

What is the major goal of supervised learning?
To find parameter values that will allow a model to perform well on historical data and to make predictions on unknown data that have benn part of the training dataset.

Einführung Data Science

What is meant by Classification?
• the process of modeling and predicting a discrete class label that is often not measurable but observable
• two classes to predict, usually 1 or 0 values, then it is called binary classification
• when there are more than two class labels to predict it is called multi-classification
• examples eg predicting employee churn, email spam, financial fraud, student letter grades

Einführung Data Science

What is the problem with hyperparameters?
There is no magic number that work everywhere

The best hyperparameter setting for a specific learning algorithm specifically depends on each learning task and on each dataset!

Einführung Data Science

What are approaches inherited from the hyperparameter optimization?
grid search and random search

Einführung Data Science

What fundamental problem lies ahead when searching for the right learning rate?
• high values for the learning rate often lead to overshooting or oscillation around the minimum
• very low values often slowing down the process of reaching the minimum and/or tapping the algorithm in an undesirable local minimum

Einführung Data Science

What does iterative optimization algorithm mean?
to pass over the training set for a multiple of times in a way that can also be controlled via a series of additional hyperparameters in order to get the best result

Einführung Data Science

To what refers Iterations?
The number of batches need to complete one epoch

For a training set with 1000 instances and a batch size of 250 it would take 4 iterations to complete 1 epoch

Einführung Data Science

What are the main problems of the Batch Gradient Descent?
• the batch gradient descent passes over the entire training set before taking only a single small step in the direction of steepest descent
• therefore it is a very costly operation especially if the training set is large
• another problem with gradient descent algorithms in general and with the batch gradient descent in particular is that they are susceptible to local minima

Einführung Data Science

What ensures strict convexity?
That the only local optimum of a function is global at the same time

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