A practical guide to machine learning for actuaries · What I learned about machine learning 1....
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17 OCTOBER 2018 Dave Liner A practical guide to machine learning for actuaries
Transcript of A practical guide to machine learning for actuaries · What I learned about machine learning 1....
17 OCTOBER 2018
Dave Liner
A practical guide to machine learning for actuaries
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CareerCast top job rankings by year
Actuary
Rank 2009 2010 2011 2012 2013 2014 2015 2016 2017
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CareerCast top job rankings by year
Actuary
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 data
2. Prepare data
3. Build model
4. Use model to gain insight
5. Tell others about model results
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We will review five machine learning methods today
k-Nearest neighbors
k-Means clustering
Decision trees
Logistic regression
Neural networks
Illustrative dataset
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X1 = Petal length
X2 = Petal width
Y = Species
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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?
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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?
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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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Unsupervised learning does not require labels (Y)
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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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Step 3: keep moving centroids until no points are reassigned
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Step 4: assign each point to a centroid cluster
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Step 4: assign each point to a centroid cluster
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Versicolor
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Step 5: use clusters to assign new data points
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Step 5: use clusters to assign new data points
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Step 5: use clusters to assign new data points
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Petal Width
Petal Length
Setosa
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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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Decision trees provide another method for classification
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Virginica
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Decision trees provide another method for classification
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Decision trees provide another method for classification
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Petal Length
Setosa
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Virginica
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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
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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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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?
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Petal Width
Petal Length
Setosa
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Virginica?
Petal Width
Yes
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Is it virginica?
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Petal Length
Setosa
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Virginica
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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 Networks
Input Layer Hidden Layer Output Layer
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Neural Networks
Input Layer Hidden Layer Output Layer
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Neural Networks
Input Layer Hidden Layer Output Layer
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Neural Networks
Input 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 happens
Predict opioid drug abuse before it happens
Estimate member persistency based on member and dependent characteristics
Project medical costs using personal and clinical data
Develop clinical best-practices by linking clinical and financial data
