Samenvatting
Summary Data Science Methods in Finance
- Instelling
- Tilburg University (UVT)
Summary of the Lectures including R codes of lecture slides
[Meer zien]Voorbeeld 4 van de 55 pagina's
Voorbeeld 4 van de 55 pagina's
In winkelwagengesponsord bericht van onze partner
Gekoppeld boek
Titel boek:
Auteur(s):
- Uitgave:
- ISBN:
- Druk:
Verkoper
Volgen
Voorbeeld van de inhoud
Summary Data Science MethodsContents
Week 1..................................................................................................................................................... 5
Notations ............................................................................................................................................. 5
Supervised learning ............................................................................................................................. 6
Why estimate f? Prediction vs Inference ........................................................................................ 7
Parametric vs Non-parametric Methods ......................................................................................... 8
Smooth Spline.................................................................................................................................. 8
Rough Spline .................................................................................................................................... 8
Prediction Accuracy and Interpretability......................................................................................... 8
Numerical Optimization .................................................................................................................. 9
Supervised Learning: Regression vs Classification .............................................................................. 9
Unsupervised learning......................................................................................................................... 9
Assessing Model Accuracy............................................................................................................. 10
Measuring Model Accuracy ........................................................................................................... 10
Training, Validation and Test Data ................................................................................................ 10
Test Data and Model Evaluation ................................................................................................... 11
Example 1: Training and Test MSE for Non-Linear Model ............................................................ 11
Example 2: Training and Test MSE for Linear Model .................................................................... 11
Flexibility and Overfitting .............................................................................................................. 12
Bias-Variance Trade-Off ................................................................................................................ 12
Bias-Variance Trade-Off Example 1 (bias-variance.R) ................................................................... 12
Bias-Variance Trade-Off: Example 2, Flexible method .................................................................. 13
Variance of Inflexible Method ....................................................................................................... 13
Example: Bias of Inflexible Method ............................................................................................... 13
Example: Bias of Flexible Method ................................................................................................. 14
Classification Setting ..................................................................................................................... 14
Week 2 – Supervised Learning .............................................................................................................. 15
Classification ...................................................................................................................................... 15
Linear Regression recap ................................................................................................................ 15
Logistic Regression ........................................................................................................................ 15
Maximum Likelihood ......................................................................................................................... 16
Logistic regression with several variables ..................................................................................... 17
Multiclass Logistic Regression ....................................................................................................... 17
1
, Resampling Methods ......................................................................................................................... 17
Recap: Bias-Variance Trade-Off ..................................................................................................... 17
Training Error vs. Test Error........................................................................................................... 18
Validation-set Approach ................................................................................................................ 18
Example validation: automobile data............................................................................................ 18
Test MSE: Automobile data ........................................................................................................... 19
K-fold Cross-Validation .................................................................................................................. 19
Leave-One-Out Cross-Validation (LOOVC) .................................................................................... 20
Cross-Validation for Classification Problems................................................................................. 20
Some remarks on (standard) Cross-Validation.............................................................................. 21
Validation with Time-series Data ...................................................................................................... 21
Basic Supervised Learning ................................................................................................................. 22
Supervised vs unsupervised learning ............................................................................................ 22
Prediction versus Inference ........................................................................................................... 22
Three classes of methods .............................................................................................................. 22
Method 1: Best Subset Selection .................................................................................................. 22
Remarks: Best subset selection ..................................................................................................... 23
Forward / Backward Stepwise Selection ....................................................................................... 23
Shrinkage Methods........................................................................................................................ 24
Ridge Regression ........................................................................................................................... 25
Ridge Regression: Scaling of predictors......................................................................................... 25
Normalization ................................................................................................................................ 25
The Lasso ....................................................................................................................................... 27
Selecting the Tuning Parameter .................................................................................................... 27
Comparing the Lasso and Ridge Regression .................................................................................. 27
Week 3 Tree-based Methods ................................................................................................................ 28
Big Picture...................................................................................................................................... 28
Intuition ......................................................................................................................................... 28
Terminology................................................................................................................................... 29
The tree-building process .............................................................................................................. 29
An intuitive algorithm.................................................................................................................... 30
Find the right tree size by Pruning ................................................................................................ 30
Choosing the best subtree............................................................................................................. 30
Tree algorithm ............................................................................................................................... 30
Classification Trees ........................................................................................................................ 31
Gini Index and Deviance ................................................................................................................ 31
2
, Tree R Code example:.................................................................................................................... 31
Prediction model ........................................................................................................................... 32
Summary of trees .......................................................................................................................... 33
Bagging .............................................................................................................................................. 33
Estimate the MSE .......................................................................................................................... 33
Intuition: Classification .................................................................................................................. 34
R Code: Bagging ............................................................................................................................. 34
Random Forest .............................................................................................................................. 35
R Code: Random Forest ................................................................................................................. 35
Confusion matrix & Random Forest: OOB Test Error .................................................................... 36
Tuning Random Forest .................................................................................................................. 36
R Code: Tuning Random Forest ..................................................................................................... 36
Week 4 – PCA ........................................................................................................................................ 38
Introduction ................................................................................................................................... 38
Principal Component Analysis(PCA) .............................................................................................. 38
Basic Idea ....................................................................................................................................... 38
PCA Details .................................................................................................................................... 39
PCA Notations................................................................................................................................ 40
Further PCs .................................................................................................................................... 40
Proportion Variance Explained ...................................................................................................... 40
Week – Deep Learning .................................................................................................................. 41
Introduction to deep learning ........................................................................................................... 41
What is deep learning? .................................................................................................................. 41
Why deep learning? ...................................................................................................................... 41
Applications of Deep Learning....................................................................................................... 41
Feedforward neural networks ........................................................................................................... 42
Key Building Block: The Perceptron .............................................................................................. 42
The Activation Function................................................................................................................. 42
Purple nodes combine two steps: ................................................................................................. 42
Compute Output Y^ using Neurons Zk .......................................................................................... 43
Building a Single Layer Neural Network in R using Keras (one hidden layer) ............................... 43
Dense Layer ................................................................................................................................... 44
A simple Example of a Feedforward NN........................................................................................ 44
R-Code: A simple Example of a Feedforward NN .......................................................................... 45
A simple Example: Training the NN ............................................................................................... 45
Example: Hitters Data .................................................................................................................... 45
3
, Feedforward NN for Classification Problems ................................................................................ 46
Training Neural Networks ................................................................................................................. 47
Training neural networks: Loss minimization................................................................................ 47
Numerical Optimization: Gradient Decent .................................................................................... 47
Computing Gradients: Backpropagtion ......................................................................................... 47
Loss Function of a Deep Neural Network ...................................................................................... 48
Minimization of the Loss Function ................................................................................................ 48
Choosing the Learning Rate........................................................................................................... 49
Training Neural Nets in Practice: Mini-Batches............................................................................. 49
Mini-Batches.................................................................................................................................. 50
Epochs ........................................................................................................................................... 50
A Feedforward Neural Network in R ............................................................................................. 50
Processing Text Data ..................................................................................................................... 51
Regularization for Neural Networks .................................................................................................. 51
Overfitting and Regularization ...................................................................................................... 51
1) Weight Regularization ........................................................................................................... 52
2) Dropout ................................................................................................................................. 52
3) Early Stopping ........................................................................................................................ 53
Task of improving the Neural Network, how? .............................................................................. 53
Neural Nets in Practice ...................................................................................................................... 54
Multi-Output Neural Nets ............................................................................................................. 54
Choosing the Last-Layer Activation and Loss Function ................................................................. 54
Initializing the Weights .................................................................................................................. 54
Exploding and Vanishing Gradients ............................................................................................... 55
Possible Solutions .......................................................................................................................... 55
Network Architecture in Practice .................................................................................................. 55
Outlook .......................................................................................................................................... 55
4
Voordelen van het kopen van samenvattingen bij Stuvia op een rij:
Verzekerd van kwaliteit door reviews
Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!
Snel en makkelijk kopen
Je betaalt supersnel en eenmalig met iDeal, creditcard of Stuvia-tegoed voor de samenvatting. Zonder lidmaatschap.
Focus op de essentie
Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!
Veelgestelde vragen
Wat krijg ik als ik dit document koop?
Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.
Tevredenheidsgarantie: hoe werkt dat?
Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.
Van wie koop ik deze samenvatting?
Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper FinanceTillie. Stuvia faciliteert de betaling aan de verkoper.
Zit ik meteen vast aan een abonnement?
Nee, je koopt alleen deze samenvatting voor €6,99. Je zit daarna nergens aan vast.
Is Stuvia te vertrouwen?
4,6 sterren op Google & Trustpilot (+1000 reviews)
Afgelopen 30 dagen zijn er 52355 samenvattingen verkocht
Opgericht in 2010, al 14 jaar dé plek om samenvattingen te kopen