Python at FernUniversität in Hagen

Flashcards and summaries for Python at the FernUniversität in Hagen

Arrow Arrow

It’s completely free

studysmarter schule studium
d

4.5 /5

studysmarter schule studium
d

4.8 /5

studysmarter schule studium
d

4.5 /5

studysmarter schule studium
d

4.8 /5

Study with flashcards and summaries for the course Python at the FernUniversität in Hagen

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

Which supervised regression models have metric-continuous variables?

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

What key benefit comes with the Logistic Regression Model?

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

Which bias and variance has Underfitting?

Select the correct answers:

  1. high variance

  2. low variance

  3. high bias

  4. low bias

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

How does regularization tries to reduce overfitting in regression models?

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

the gradient descent...

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

What regularization method implicitly conducts a feature selection by setting unimportant parameters to 0?

Select the correct answers:

  1. LASSO (Least Absolute Shrinkage and Selection Operator) – L1

  2. L2

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

What indicates a low prediction error on the training set and a high prediction error on the test set?

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

Which variance and bias has Overfitting?

Select the correct answers:

  1. low bias

  2. high variance

  3. high bias

  4. low variance

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

On what is a single logistic regression model not applicable if the target is of categorical scale?

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

What is the functional value y of the logistic / sigmoid function?

Select the correct answers:

  1. Always between 0 and 1 for any given value of x

  2. Always between 0 and 100 for any given value of x

  3. Always between 0 and 1000 for any given value of x

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

Main features of the Logistic Function

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

What is a regression model called that uses a logistic function to model a binary target?

Your peers in the course Python at the FernUniversität in Hagen create and share summaries, flashcards, study plans and other learning materials with the intelligent StudySmarter learning app.

Get started now!

Flashcard Flashcard

Exemplary flashcards for Python at the FernUniversität in Hagen on StudySmarter:

Python

Which supervised regression models have metric-continuous variables?

Linear Regression

Python

What key benefit comes with the Logistic Regression Model?

Allows us to perform classification tasks

Python

Which bias and variance has Underfitting?
  1. high variance

  2. low variance

  3. high bias

  4. low bias

Python

How does regularization tries to reduce overfitting in regression models?

Regularisation takes out unimportant features and therefore reduces the complexity of the model

Python

the gradient descent...

…is an iterative optimization algorithm which means that in order to get the best result it is necessary to pass over the training set for a multiple of times in a way

Python

What regularization method implicitly conducts a feature selection by setting unimportant parameters to 0?
  1. LASSO (Least Absolute Shrinkage and Selection Operator) – L1

  2. L2

Python

What indicates a low prediction error on the training set and a high prediction error on the test set?

The model has overfitted data

Python

Which variance and bias has Overfitting?
  1. low bias

  2. high variance

  3. high bias

  4. low variance

Python

On what is a single logistic regression model not applicable if the target is of categorical scale?

On variables with more than two distinct characters
Solution: You must use the multi-class logistic regression

Python

What is the functional value y of the logistic / sigmoid function?
  1. Always between 0 and 1 for any given value of x

  2. Always between 0 and 100 for any given value of x

  3. Always between 0 and 1000 for any given value of x

Python

Main features of the Logistic Function
  1. Exists Due to the binary classification problem
  2. Idea: Squash the output of the linear regression hypothesis into a range between 0 and 1
  3. To do so: make use of the logistic (or sigmoid) function

Python

What is a regression model called that uses a logistic function to model a binary target?

Logistic regression model​​​​

Sign up for free to see all flashcards and summaries for Python at the FernUniversität in Hagen

Singup Image Singup Image
Wave

Other courses from your degree program

For your degree program Psychology at the FernUniversität in Hagen there are already many courses on StudySmarter, waiting for you to join them. Get access to flashcards, summaries, and much more.

Back to FernUniversität in Hagen overview page

Kapitel 2 - 2.Grundlagen

Kapitel 2 - 2.Diagnostische Psy

Kapitel 4 - 2.Grundlagen der Diagnostik

Kapitel 6 - 2.Grundlagen der Diagnostischen Psy.

Kapitel 7 - 2.Grundlagen der Diagnostischen Psy.

Kapitel 8 - 2.Grundlagen der Diagnostischen Psychologie

Kapitel 9 - 2.Grundlagen der Diagnostischen Psychologie

Einführung in die Psychologie, ihre Methoden und Techniken wissenschaftlichen Arbeitens

Statistik

Biologische Psychologie und Allgemeine Psychologie II: Lernen, Motivation, Emotion

Entwicklungspsychologie

dkakndoiw

CP - Einführung in die CP

Einführung in die CP K2

Einführung in die CP K2

Korrelation (Literatur: Sedlmeier)

STATISTIK (Literatur: Sedlmeier)

CP Einführung - K4 - Empowerment

CP Einführung - K4 - Empowerment

CP Einführung - K6 - CP Forschung und Anwendung

CP - Methoden - K1 - Einführung und Grundlagen der CP Forschung

CP - Methoden - K2 - Datenerhebung, Analyse und Ableitung von Empfehlungen

CP - Methoden - K2 - Datenerhebung, Analyse und Ableitung von Empfehlungen

STATISTIK - 9 EFFEKTGRÖßEN

STATISTIK 1 EINFÜHRUNG

CP - Methoden - K4 - Ausgewählte Methoden der CP

CP - Themen - Akkulturationsforschung

STATISTIK 2 UNIVARIATE DESKRIPTIVE STATISTIK

CP - Themen - Radikalisierung

CP - Themen - Frieden und Versöhnung

CP - Themen - Umweltschutz

CP - Themen - CP Perspektive auf das Altern

CP - Themen - Flucht und Integration

CP - Themen - Interkulturelle Kompetenz

Statistik 4 Grundlagen der Inferenzstatistik

CP - Themen - Akkulturation

STATISTIK 5 KONFIDENZINTERVALL UND SIGNIFIKANZTEST

CP - Themen - Politische Psychologie

STATISTIK 13 U-TEST - WILCOXON TEST

STATISTIK 16 BAYES

STATISTIK 7 T-TEST

STATISTIK 8 EINFAKTORIELLE Varianzanalyse

M5 Entwicklungspsychologie

M5 Siegler Einführung I

M1 Psychologie (Gerrig)

Psychologie und kulturelle Vielfalt

Diagnostik

M1 Einführung Psych VL

CP - Quizfragen moodle

M1 Kulturelle Vielfalt

M1 Methoden

Glossar Gerrig

Gerrig Einführung

Einführung in die Psychologie

M1.1 8 Kognitive Prozesse

M1.1 8 Kognitive Prozesse

Einführung in die Forschungsmethoden

M1.1 9. Emotionen und Motivation

M2 - Statistik

R- STUDIO

1. Psychologie als Wissenschaft

2. Forschungsmethoden der Psychologie

5. Bewusstsein

Bayesianische Statistik

Psychologie und kulturelle Vielfalt

Einführung in die Psychologie

M1.15 Sozialpsychologie

M1 K1 U3 Biol. und evolut. Grdlg

Einfügrung in die Psychologie

MOTIVATION/ EMOTION

Psychoneuroendokrinologie

Intrinsische Motivation

Superquiz M1

Einführung in die Forschungsmethoden der Psychologie

Psychologie Unit 3

Synaptische Erregung & Hemmung

Synaptische Transmitter und Modulatoren

Ziele auswählen und umsetzen

10 Leistungsmotiv/Training

Alltagsgedächtnis

Motivation - Volition

ATTRIBUTION

Rolle von Erleben, Physiologie und Interpretation

Emotionsausdruck

Emotion und neuronale Grundlagen

bildungspsychologie

Emotionales Konditionieren

Fragebogen- und Testkonstruktion

Englisch Grundwortschatz

Französisch Grundwortschatz

Biopsychologie 1

Biopsychologie 2

Statistik

M3b Biologische Psychologie

Psychologie als Wissenschaft

Allgemeine Thema 1 Wahrnehmung

M1 Allgemeine Psychologie

schlüsselbegriffe Modul 1

Allgemeine Thema Aufmersamkeit&leistung

M1 Psychologie und kulturelle Vielfalt

Die menschliche Persönlichkeit

Einführung Statistik

What is StudySmarter?

What is StudySmarter?

StudySmarter is an intelligent learning tool for students. With StudySmarter you can easily and efficiently create flashcards, summaries, mind maps, study plans and more. Create your own flashcards e.g. for Python at the FernUniversität in Hagen or access thousands of learning materials created by your fellow students. Whether at your own university or at other universities. Hundreds of thousands of students use StudySmarter to efficiently prepare for their exams. Available on the Web, Android & iOS. It’s completely free.

Awards

Best EdTech Startup in Europe

Awards
Awards

EUROPEAN YOUTH AWARD IN SMART LEARNING

Awards
Awards

BEST EDTECH STARTUP IN GERMANY

Awards
Awards

Best EdTech Startup in Europe

Awards
Awards

EUROPEAN YOUTH AWARD IN SMART LEARNING

Awards
Awards

BEST EDTECH STARTUP IN GERMANY

Awards

How it works

Top-Image

Get a learning plan

Prepare for all of your exams in time. StudySmarter creates your individual learning plan, tailored to your study type and preferences.

Top-Image

Create flashcards

Create flashcards within seconds with the help of efficient screenshot and marking features. Maximize your comprehension with our intelligent StudySmarter Trainer.

Top-Image

Create summaries

Highlight the most important passages in your learning materials and StudySmarter will create a summary for you. No additional effort required.

Top-Image

Study alone or in a group

StudySmarter automatically finds you a study group. Share flashcards and summaries with your fellow students and get answers to your questions.

Top-Image

Statistics and feedback

Always keep track of your study progress. StudySmarter shows you exactly what you have achieved and what you need to review to achieve your dream grades.

1

Learning Plan

2

Flashcards

3

Summaries

4

Teamwork

5

Feedback