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Data Science Crash Course
1.
Data Science Crash Course - DataWorks
Summit - Munich 2017 Robert Hryniewicz Developer Advocate @RobertH8z rhryniewicz@hortonworks.com
2.
2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved What is Data Science? Ã
Extracting knowledge/insights from data – Data: structured or unstructured à Continuation of – statistics – machine learning – data mining – predictive analytics
3.
3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved What is Machine Learning? Machine Learning “science of how computers learn without being explicitly programmed”
4.
4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved “AI is the new electricity.” “AI needs to be company wide strategic decision.” Andrew Ng Chief Data Scientist Co-founder of Coursera Prof. at Stanford
5.
5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved A Brief History of AI Antiquity –
An Ancient Wish to Forge the Gods 1940 (Digital Computer, scientists discuss electronic brain) 1954 – 73 (Marvin Minsky et al. in Dartmouth College) 1973 – 80 1980 – 87 (Japanese gov.) 1987 – 93 1993 – 2000 2000 à Present
6.
6 © Hortonworks Inc. 2011 – 2016. All Rights Reserved AI in Media & Pop Culture
7.
7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
8.
8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
9.
9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
10.
10 © Hortonworks Inc. 2011 – 2016. All Rights Reserved What is AI? Ã
General or Pure AI Ã Narrow or Pragmatic AI
11.
11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
12.
12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved “Big Data” Ã
Internet of Anything (IoT) – Wind Turbines, Oil Rigs – Beacons, Wearables – Smart Cars à User Generated Content (Social, Web & Mobile) – Twitter, Facebook, Snapchat – Clickstream – Paypal, Venmo 44ZB in 2020
13.
13 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Visualizing 44ZB 100 pixels = 1M TB 100 px
-> 1M TB assumes 5M pixel resolution screen
14.
14 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
15.
15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
16.
16 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Key drivers behind AI Explosion Ã
Exponential data growth à Faster distributed systems à Smarter algorithms
17.
17 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Major Trends in AI Technologies Ã
Knowledge Engineering à Machine Learning à Deep Learning à Image Analysis à Natural Language Processing & Generation à Robotics & Automation
18.
18 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Creating Value with AI Ã
Cognitive insights à Cognitive engagement à Cognitive automation
19.
19 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Machine
Learning Use Cases Healthcare Predict diagnosis Prioritize screenings Reduce re-admittance rates Financial services Fraud Detection/prevention Predict underwriting risk New account risk screens Public Sector Analyze public sentiment Optimize resource allocation Law enforcement & security Retail Product recommendation Inventory management Price optimization Telco/mobile Predict customer churn Predict equipment failure Customer behavior analysis Oil & Gas Predictive maintenance Seismic data management Predict well production levels
20.
20 © Hortonworks Inc. 2011 – 2016. All Rights Reserved What Is Apache Spark? Ã
Apache open source project originally developed at AMPLab (University of California Berkeley) Ã Unified data processing engine that operates across varied data workloads and platforms
21.
21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Why Apache Spark? Ã
Elegant Developer APIs – Single environment for data munging, data wrangling, and Machine Learning (ML) à Fast! - In-memory computation model – Effective for iterative computations à Machine Learning – Implementation of distributed ML algorithms
22.
22 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Spark SQL Structured Data Spark Streaming Near Real-time Spark MLlib Machine Learning GraphX Graph Analysis
23.
23 © Hortonworks Inc. 2011 – 2016. All Rights Reserved More Flexible
Better Storage and Performance///
24.
24 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Spark SQL Overview Ã
Spark module for structured data processing (e.g. DB tables, JSON files, CSV) Ã Three ways to manipulate data: – DataFrames API – SQL queries – Datasets API
25.
25 © Hortonworks Inc. 2011 – 2016. All Rights Reserved DataFrames Ã
Distributed collection of data organized into named columns à Conceptually equivalent to a table in relational DB or a data frame in R/Python à API available in Scala, Java, Python, and R Col1 Col2 … … ColN DataFrame Column Row Data is described as a DataFrame with rows, columns, and a schema
26.
26 © Hortonworks Inc. 2011 – 2016. All Rights Reserved DataFrames CSVAvro HIVE Spark SQL Col1
Col2 … … ColN DataFrame Column Row JSON
27.
27 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Visualizations
28.
28 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Source: commons.wikimedia.org/w/index.php?curid=17857442
29.
29 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Data Visualization: Twitter Source: https://medium.com/@swainjo/us-presidential-election-2016-twitter-analysis-7596606853e5#.dozwu2bhd
30.
30 © Hortonworks Inc. 2011 – 2016. All Rights Reserved imple line chart
31.
31 © Hortonworks Inc. 2011 – 2016. All Rights Reserved orizontal plot of three line charts
32.
32 © Hortonworks Inc. 2011 – 2016. All Rights Reserved treaming data into a line chart
33.
33 © Hortonworks Inc. 2011 – 2016. All Rights Reserved lotting Iris data features in one plot
34.
34 © Hortonworks Inc. 2011 – 2016. All Rights Reserved omparing Iris data distributions
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35 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Spark SQL Structured Data Spark Streaming Near Real-time Spark MLlib Machine Learning GraphX Graph Analysis
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36 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Algorithms
37.
37 © Hortonworks Inc. 2011 – 2016. All Rights Reserved What is a ML Model? Ã
Mathematical formula with a number of parameters that need to be learned from the data. And fitting a model to the data is a process known as model training à E.g. linear regression – Goal: fit a line y = mx + c to data points – After model training: y = 2x + 5 Input OutputModel 1, 0, 7, 2, … 7, 5, 19, 9, …
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38 © Hortonworks Inc. 2011 – 2016. All Rights Reserved START Regression Classification
Collaborative Filtering Clustering Dimensionality Reduction • Logistic Regression • Support Vector Machines (SVM) • Random Forest (RF) • Naïve Bayes • Linear Regression • Alternating Least Squares (ALS) • K-Means, LDA • Principal Component Analysis (PCA)
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39 © Hortonworks Inc. 2011 – 2016. All Rights Reserved CLASSIFICATION Identifying to which category an object belongs to Examples: spam detection, diabetes diagnosis, text labeling Algorithms: Ã
Logistic Regression – Fast training, linear model – Classes expressed in probabilities à Support Vector Machines (SVM) – “Best” supervised learning algorithm, effective – More robust to outliers than Log Regression – Handles non-linearity à Random Forest – Fast training – Handles categorical features – Does not require feature scaling – Captures non-linearity and feature interaction à Naïve Bayes – Good for text classification – Assumes independent variables
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40 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Visual Intro to Decision Trees Ã
http://www.r2d3.us/visual-intro-to-machine-learning-part-1 CLASSIFICATION
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41 © Hortonworks Inc. 2011 – 2016. All Rights Reserved REGRESSION Predicting a continuous-valued output Example: Predicting
house prices based on number of bedrooms and square footage Algorithms: Linear Regression
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42 © Hortonworks Inc. 2011 – 2016. All Rights Reserved CLUSTERING Automatic grouping of similar objects into sets (clusters) Example: market segmentation –
auto group customers into different market segments Algorithms: K-means, LDA
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43 © Hortonworks Inc. 2011 – 2016. All Rights Reserved COLLABORATIVE FILTERING Fill in the missing entries of a user-item association matrix Applications: Product/movie recommendation Algorithms:
Alternating Least Squares (ALS)
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44 © Hortonworks Inc. 2011 – 2016. All Rights Reserved DIMENSIONALITY REDUCTION Reducing the number of redundant features/variables Applications: Ã
Removing noise in images by selecting only “important” features à Removing redundant features, e.g. MPH & KPH are linearly dependent Algorithms: Principal Component Analysis (PCA)
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45 © Hortonworks Inc. 2011 – 2016. All Rights Reserved START Regression Classification
Deep Learning Clustering Dimensionality Reduction • XGBoost (Extreme Gradient Boosting) • Classification and regression trees (CART) • Recurrent Neural Network (RNN) • Convolutional Neural Network (CNN) • Yinyang K-Means • t-Distributed Stochastic Neighbor Embedding (t-SNE) • Local Regression (LOESS) Collaborative Filtering • Weighted Alternating Least Squares (WALS)
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46 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
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47 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Hyperparameters Ã
Define higher-level model properties, e.g. complexity or learning rate à Cannot be learned during training à need to be predefined à Can be decided by – setting different values – training different models – choosing the values that test better à Hyperparameter examples – Number of leaves or depth of a tree – Number of latent factors in a matrix factorization – Learning rate (in many models) – Number of hidden layers in a deep neural network – Number of clusters in a k-means clustering
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48 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Predictive
Analytics Pre-requisites
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49 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Predictive
Analytics Process and Tools
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50 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Asking Relevant Questions Ã
Specific (can you think of a clear answer?) Ã Measurable (quantifiable? data driven?) Ã Actionable (if you had an answer, could you do something with it?) Ã Realistic (can you get an answer with data you have?) Ã Timely (answer in reasonable timeframe?)
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51 © Hortonworks Inc. 2011 – 2016. All Rights Reserved With that in mind… Ã
No simple formula for “good questions” only general guidelines à The right data is better than lots of data à Understanding relationships matters
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52 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Data Preparation 1.
Data analysis (audit for anomalies/errors) 2. Creating an intuitive workflow (formulate seq. of prep operations) 3. Validation (correctness evaluated against sample representative dataset) 4. Transformation (actual prep process takes place) 5. Backflow of cleaned data (replace original dirty data) Approx. 80% of Data Analyst’s job is Data Preparation! Example of multiple values used for U.S. States è California, CA, Cal., Cal
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53 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Detailed Research and Operational Workflows
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54 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Training Set Learning Algorithm h hypothesis/model input
output Ingest / Enrich Data Clean / Transform / Filter Select / Create New Features Evaluate Accuracy / Score
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55 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Building
Spark ML pipelines Feature transform 1 Feature transform 2 Combine features Linear Regression Input DataFrame Input DataFrame Output DataFrame Pipeline Pipeline Model Train Predict Export Model
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56 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Spark
ML Pipeline à fit() is for training à transform() is for prediction Input DataFrame (TRAIN) Input DataFrame (TEST) Output Dataframe (PREDICTIONS) Pipeline Pipeline Model fit() transform() Train Predict
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57 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Sample
Spark ML Pipeline indexer = … parser = … hashingTF = … vecAssembler = … rf = RandomForestClassifier(numTrees=100) pipe = Pipeline(stages=[indexer, parser, hashingTF, vecAssembler, rf]) model = pipe.fit(trainData) # Train model results = model.transform(testData) # Test model
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58 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Exporting
ML Models - PMML Ã Predictive Model Markup Language (PMML) Ã Supported models –K-Means –Linear Regression –Ridge Regression –Lasso –SVM –Binary
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59 © Hortonworks Inc. 2011 – 2016. All Rights Reserved HDCloud
60.
60 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Hortonworks Cloud Solutions Microsoft
AWS Google Managed Azure HDInsight Non-Managed / Marketplace Hortonworks Data Cloud for AWS Cloud IaaS Hortonworks Data Platform (via Ambari and via Cloudbreak)
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61 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
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62 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Zeppelin Ambari Spark History Server Files View
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63 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Ã
Zeppelin è Interactive notebook à Spark à YARN è Resource Management à HDFS è Distributed Storage Layer YARN Scala Java Python R APIs Spark Core Engine Spark SQL Spark Streaming MLlib GraphX 1 ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° N HDFS
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64 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Spark
and HDP
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65 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Labs / Tutorials
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66 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Scatter
2D Data Visualized scatterData ç DataFrame +-----+--------+ |label|features| +-----+--------+ |-12.0| [-4.9]| | -6.0| [-4.5]| | -7.2| [-4.1]| | -5.0| [-3.2]| | -2.0| [-3.0]| | -3.1| [-2.1]| | -4.0| [-1.5]| | -2.2| [-1.2]| | -2.0| [-0.7]| | 1.0| [-0.5]| | -0.7| [-0.2]| ... ... ...
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67 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Linear
Regression Model Training (one feature) Coefficients: 2.81 Intercept: 3.05 y = 2.81x + 3.05 Training Result
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68 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Linear
Regression (two features) Coefficients: [0.464, 0.464] Intercept: 0.0563
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69 © Hortonworks Inc. 2011 – 2016. All Rights Reserved ML
Lab • Residuals • residual of an observed value is the difference between the observed value and the estimated value • R2 (R Squared) – Coefficient of Determination • indicates a goodness of fit • R2 of 1 means regression line perfectly fits data • RMSE (Root Mean Square Error) • measure of differences between values predicted by a model or and values actually observed • good measure of accuracy, but only to compare forecasting errors of different models (individual variables are scale-dependent)
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70 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Demo: Stock Portfolio Simulation using Monte Carlo method Monte Carlo Simulation 1.
Define a domain of possible inputs 2. Randomly generate inputs from prob. distribution over domain 3. Perform computation on the inputs 4. Aggregate the results Approximating the value of π after placing 30K random points. Error < 0.07% of actual value.
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71 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Demo: Text Classification with Naïve Bayes
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72 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Diabetes Dataset –
Decision Trees / Random Forest Labeled set with 8 Features -1 1:-0.294118 2:0.487437 3:0.180328 4:-0.292929 5:-1 6:0.00149028 7:-0.53117 8:-0.0333333 +1 1:-0.882353 2:-0.145729 3:0.0819672 4:-0.414141 5:-1 6:-0.207153 7:-0.766866 8:-0.666667 -1 1:-0.0588235 2:0.839196 3:0.0491803 4:-1 5:-1 6:-0.305514 7:-0.492741 8:-0.633333 +1 1:-0.882353 2:-0.105528 3:0.0819672 4:-0.535354 5:-0.777778 6:-0.162444 7:-0.923997 8:-1 -1 1:-1 2:0.376884 3:-0.344262 4:-0.292929 5:-0.602837 6:0.28465 7:0.887276 8:-0.6 +1 1:-0.411765 2:0.165829 3:0.213115 4:-1 5:-1 6:-0.23696 7:-0.894962 8:-0.7 -1 1:-0.647059 2:-0.21608 3:-0.180328 4:-0.353535 5:-0.791962 6:-0.0760059 7:-0.854825 8:-0.833333 ...
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73 © Hortonworks Inc. 2011 – 2016. All Rights Reserved TensorFlowOnSpark
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74 © Hortonworks Inc. 2011 – 2016. All Rights Reserved TensorFlowOnSpark
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75 © Hortonworks Inc. 2011 – 2016. All Rights Reserved TensorFlowOnSpark
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76 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Robert Hryniewicz E: rhryniewicz@hortonworks.com T: @robertH8z
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77 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Feature Selection
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78 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Feature Selection Ã
Also known as variable or attribute selection à Why important? – simplification of models è easier to interpret by researchers/users – shorter training times – enhanced generalization by reducing overfitting à Dimensionality reduction vs feature selection – Dimensionality red: create new combinations of attributes – Feature selection: include/exclude attributes in data without changing them Q: Which features should you use to create a predictive model?
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79 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Feature Selection Ã
Methods – Filter – Wrapper – Embedded Goal: Identify and remove unneeded, irrelevant and redundant features from data that do not contribute or may decrease the accuracy of a predictive model.
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80 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Feature Selection Traps Ã
Feature selection is another key part of the applied machine learning process, like model selection. You cannot fire and forget. Ã It is important to consider feature selection a part of the model selection process. If you do not, you may inadvertently introduce bias into your models which can result in overfitting. Ã For example, you must include feature selection within the inner-loop when you are using accuracy estimation methods such as cross-validation. This means that feature selection is performed on the prepared fold right before the model is trained. A mistake would be to perform feature selection first to prepare your data, then perform model selection and training on the selected features.
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81 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Feature Selection Checklist 1.
Do you have domain knowledge? If yes, construct a better set of “ad hoc” features 2. Are your features commensurate? If no, consider normalizing them. 3. Do you suspect interdependence of features? If yes, expand your feature set by constructing conjunctive features or products of features, as much as your computer resources allow you. 4. Do you need to prune the input variables (e.g. for cost, speed or data understanding reasons)? If no, construct disjunctive features or weighted sums of feature 5. Do you need to assess features individually (e.g. to understand their influence on the system or because their number is so large that you need to do a first filtering)? If yes, use a variable ranking method; else, do it anyway to get baseline results. 6. Do you need a predictor? If no, stop 7. Do you suspect your data is “dirty” (has a few meaningless input patterns and/or noisy outputs or wrong class labels)? If yes, detect the outlier examples using the top ranking variables obtained in step 5 as representation; check and/or discard them. 8. Do you know what to try first? If no, use a linear predictor. Use a forward selection method with the “probe” method as a stopping criterion or use the 0- norm embedded method for comparison, following the ranking of step 5, construct a sequence of predictors of same nature using increasing subsets of features. Can you match or improve performance with a smaller subset? If yes, try a non-linear predictor with that subset. 9. Do you have new ideas, time, computational resources, and enough examples? If yes, compare several feature selection methods, including your new idea, correlation coefficients, backward selection and embedded methods. Use linear and non-linear predictors. Select the best approach with model selection 10. Do you want a stable solution (to improve performance and/or understanding)? If yes, subsample your data and redo your analysis for several “bootstrap”.
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82 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Robert Hryniewicz E: rhryniewicz@hortonworks.com T: @robertH8z
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83 © Hortonworks Inc. 2011 – 2016. All Rights Reserved AI Investment Landscape
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84 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Only $100k investment needed to start with AI
85.
85 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Report
from IDC Analyst firm Spending on AI • $12.5B in 2017 • $4.5B on apps for threat detection, fraud analysis, public safety, and pharmaceutical research • $46B+ by 2020
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86 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Closing thoughts on AI
87.
87 © Hortonworks Inc. 2011 – 2016. All Rights Reserved The Future of Cognitive Computing / MI –
Machine • Deep Learning • Discovery • Large-scale math • Fact checking – Human • Compassion • Intuition • Design • Value judgements • Common Sense
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88 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
89.
89 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Robert Hryniewicz E: rhryniewicz@hortonworks.com T: @robertH8z
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90 © Hortonworks Inc. 2011 – 2016. All Rights Reserved What’s new in HDP 2.6 –
Spark & Zeppelin à Spark 1.6.3 GA à Spark 2.1 GA à REST API (Livy) GA à Spark Thrift Server doAS GA à SparkSQL – Row/Column Security (GA) à Spark Streaming + Kafka over SSL à Multi Cluster HBase support for SHC à Package support in PySpark & SparkR Spark à Spark 2.x support à Improved Livy integration à No password in clear à JDBC interpreter improvements à Smart Sense integration à Knox proxy Zeppelin UI Zeppelin 0.7.x
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91 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Thanks! Robert Hryniewicz @RobertH8z rhryniewicz@hortonworks.com
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