Session 124 Teaching Session: A Practical Guide to Machine ... learning drives disruption and...
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Session 124TS, A Practical Guide to Machine Learning for Actuaries Presenters: Dave M. Liner, FSA, MAAA, CERA SOA Antitrust Disclaimer SOA Presentation Disclaimer
Transcript of Session 124 Teaching Session: A Practical Guide to Machine ... learning drives disruption and...
Session 124TS, A Practical Guide to Machine Learning for Actuaries
Presenters: Dave M. Liner, FSA, MAAA, CERA
SOA Antitrust Disclaimer SOA Presentation Disclaimer
27 JUNE 2018
Dave Liner
A practical guide to machine learning for actuaries
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Data Scientist
Statistician
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What I learned about machine learning
1. Machine learning drives disruption and innovation in many sectors globally
2. Many actuaries already do most of the machine learning process
3. Most popular machine learning methods are based on concepts that actuaries already understand
4. There is a staggering amount of free resources to develop machine learning skills
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Actuaries already do most of the machine learning process
1. Get data2. Prepare data3. Build model4. Use model to gain insight5. Tell others about model results
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We will review five machine learning methods today
k-Nearest neighborsk-Means clusteringDecision treesLinear regressionLogistic regressionNeural networks
Illustrative dataset
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X1 = Petal lengthX2 = Petal widthY = Species
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0.0
0.5
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2.5
1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Versicolor
Virginica
It is easier to illustrate methods using 2-dimensional data
k-Nearest neighbors
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What is the species of a new sample based on Petal width and length using kNN?
0.0
0.5
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1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Virginica
Versicolor
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What is the species of a new sample based on petal width and length using kNN?
0.0
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2.0
2.5
1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Virginica
Versicolor
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Four lines of Python code gets same result as Excel kNN file
import sklearn.datasets as ds, sklearn.model_selection as ms, sklearn.preprocessing as pp, sklearn.neighbors, sklearn.metrics
X_train,X_test,y_train,y_test = ms.train_test_split(pp.scale(ds.load_iris().data),ds.load_iris().target,test_size=30,random_state=0)
y_pred_kNN = sklearn.neighbors.KNeighborsClassifier(n_neighbors=3).fit(X_train,y_train).predict(X_test)
print(sklearn.metrics.confusion_matrix(y_test, y_pred_kNN))
k-Means clustering
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What is the species of a new sample based on Petal width and length using clustering?
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Petal Width
Petal Length
Unsupervised learning does not require labels (Y)
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Petal Width
Petal Length24
Step 1: select random centroids (k=3 in this case)
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Step 2: move centroids based on data
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Petal Width
Petal Length
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Step 3: keep moving centroids until no points are reassigned
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Petal Width
Petal Length
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Step 4: assign each point to a centroid cluster
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Petal Width
Petal Length
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Step 4: assign each point to a centroid cluster
0.0
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Petal Width
Petal Length
Setosa
Virginica
Versicolor
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Step 5: use clusters to assign new data points
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1.0
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2.0
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1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Virginica
Versicolor
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Step 5: use clusters to assign new data points
0.0
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1.0
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2.0
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1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Virginica
Versicolor
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Step 5: use clusters to assign new data points
0.0
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2.0
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1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Virginica
Versicolor
Decision trees
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Decision trees provide another method for classification
Setosa VirginicaVersicolor
Root Node
Decision Node
Leaf Nodes
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Decision trees provide another method for classification
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Petal Width
Petal Length
Setosa
Versicolor
Virginica
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Decision trees provide another method for classification
0.0
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1.0
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2.0
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1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Versicolor
Virginica
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Decision trees provide another method for classification
0.0
0.5
1.0
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2.0
2.5
1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Versicolor
Virginica
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Decision trees provide another method for classification
0.0
0.5
1.0
1.5
2.0
2.5
1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Versicolor
Virginica
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Decision trees provide another method for classification
0.0
0.5
1.0
1.5
2.0
2.5
1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Versicolor
Virginica
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Decision trees provide another method for classification
0.0
0.5
1.0
1.5
2.0
2.5
1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Versicolor
Virginica
Logistic regression
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Linear regression uses line of best fit to make numerical predictions
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Petal Width
Petal Length
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Logistic regression is a classification algorithm
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Is it Virginica?
Petal Width
Setosa
Versicolor
Virginica
Yes
No
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Is it virginica?
0.0
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1.0
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2.0
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1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Versicolor
Virginica
0.0
0.5
1.0
0.0 0.5 1.0 1.5 2.0 2.5
Virginica?
Petal Width
Yes
No
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Is it virginica?
0.0
0.5
1.0
1.5
2.0
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1.0 2.0 3.0 4.0 5.0 6.0 7.0
Petal Width
Petal Length
Setosa
Versicolor
Virginica
0.0
0.5
1.0
0.0 0.5 1.0 1.5 2.0 2.5
Virginica?
Petal Width
Yes
No
Neural networks
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Neural can model non-linear situations
Setosa
Versicolor
Virginica
Sepal Length
Petal Width
Sepal Width
Petal Length
Activation Functions
Input Hidden Output
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Neural NetworksInput Layer Hidden Layer Output Layer
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Neural NetworksInput Layer Hidden Layer Output Layer
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Neural NetworksInput Layer Hidden Layer Output Layer
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Neural NetworksInput Layer Hidden Layer Output Layer
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Summary of methods
Unsupervised Deep Regression
k-Nearest Neighbors
k-Means Clustering
Decision Trees
Linear Regression
Logistic Regression
Neural Networks
Next steps
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Possible machine learning applications in healthcare include
Predicting non-adherent drug event before it happensPredict opioid drug abuse before it happensEstimate member persistency based on member and dependent characteristicsProject medical costs using personal and clinical dataDevelop clinical best-practices by linking clinical and financial data
