Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
The decision tree is one of the topics of the Bigdata analytics which is a subject of 8th sem CSE students. Book referred is data analytics by Anil Maheshwari.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Decision Trees for Classification: A Machine Learning AlgorithmPalin analytics
Decision Trees in Machine Learning - Decision tree method is a commonly used data mining method for establishing classification systems based on several covariates or for developing prediction algorithms for a target variable.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
The decision tree is one of the topics of the Bigdata analytics which is a subject of 8th sem CSE students. Book referred is data analytics by Anil Maheshwari.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Decision Trees for Classification: A Machine Learning AlgorithmPalin analytics
Decision Trees in Machine Learning - Decision tree method is a commonly used data mining method for establishing classification systems based on several covariates or for developing prediction algorithms for a target variable.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
4_1_Tree World.pdf
1. Leonardo Auslender Copyright 2004
Leonardo Auslender
10/3/2019
Tree World.
By Leonardo Auslender.
Copyright 2019.
Leonardo ‘dot’ auslender ‘at’ gmail ‘dot’ com
2. Leonardo Auslender Copyright 2004
Leonardo Auslender 2
10/3/2019
Contents
Varieties of trees
CART algorithm.
Tree variable selection
Tree Pruning
Tree variable importance.
Tree model diagnostics.
Sections marked with *** can be skipped at first reading.
3. Leonardo Auslender Copyright 2004 Ch. 1.4-3
10/3/2019
Varieties of Tree Methodologies.
CART
Tree (S+)
AID
THAID
CHAID ID3
C4.5
C5.0
We’ll focus on CART methodology
4. Leonardo Auslender Copyright 2004
10/3/2019
Basic References.
Breiman L. et al, 1984.
Quinlan J. (1993).
“Easy Reading” Auslender L. (1998, 1999, 2000a, 2001)
Bayesian Perspective: Chipman et al (1998).
Many, many other references.
5. Leonardo Auslender Copyright 2004
10/3/2019
Basic CART Algorithm: binary dependent
variable or target (0,1): Classification Trees.
Range of Continuous Variable A
“0”
“0”
70%
“1”
“1”
20%
50%
Original % of ‘0’s and ‘1’s of dep. var
Splitting point
With continuous dep var, decrease in variance
from root to nodes: Regression Trees.
6. Leonardo Auslender Copyright 2004 Ch. 1.4-6
10/3/2019
Divide and Conquer: recursive
partitioning.
n = 5,000
10% Event
n = 3,350 n = 1,650
Debits < 19
yes no
21% Event
5% Event
7. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 1.4-7
10/3/2019
Ideal SAS code to find splits (for those who
dare).
Proc summary data = …. Nway;
class (all independent vars);
var depvar;
output out = ….. Sum = ;
run;
For large data sets (large N, large p),
hardware and software constraints may
prevent completion.
Binary Case
8. Leonardo Auslender Copyright 2004 Ch. 1.4-8
10/3/2019
Fitted Decision Tree: Interpretation
and structure.
VAR C
>1
25%
0-52
45%
VAR B
VAR A
<19 19
5%
0,1
21%
>52
9. Leonardo Auslender Copyright 2004 Ch. 1.4-9
10/3/2019
Cultivation of Trees.
• Split Search
– Which splits are to be considered?
• Splitting Criterion
– Which split is best?
• Stopping Rule
– When should splitting stop?
• Pruning Rule
– Should some branches be lopped-off?
10. Leonardo Auslender Copyright 2004 Ch. 1.4-10
10/3/2019
Splitting Criterion: gini, twoing, misclassification, entropy,
chi-square, etc, etc. …
A) Minimize Gini impurity criterion (favors node homogeneity)
B) Maximize Twoing impurity criterion (favors class separation)
Empirical results: for binary dependent variables, Gini and Twoing are
equivalent. For trinomial, Gini provides more accurate trees. Beyond three
categories, twoing performs better.
2
r
1
l r
P
( ) [ [ ( / ) ( / )]
4
t and t : left and right nodes, respectively.
K
l
l r
k
P
i t p k t p k t
=
= −
2
1
( ) 1 ( / ) .
( / ) Cond. prob. of class k in node t.
K
k
i t Gini impurity p k t
p k t
=
= = −
=
11. Leonardo Auslender Copyright 2004
Leonardo Auslender 11
10/3/2019
Choosing between No_claims and Dr. Visits. No_claims yields lower impurity (0.237)
and split value at or below 0 is chosen. Dr. Visits impurity is 0.280.
14. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 1.4-14
10/3/2019
Tree Prediction.
Let J disjoint regions (final nodes) {R1,,,,,Rj}
Classification:
Y ε {c1, c2, ,,,, cK} , i.e., Y has K categs ➔ predictors {F1 ,,,,, FK}.
T(X) = arg max (F1 ,,,,, FK) ( category Mode is predicted value)
Regression:
Pred. Rule: obs X ε Rj ➔ T(X) = avg (yj)
15. Leonardo Auslender Copyright 2004 Ch. 1.4-15
10/3/2019
Benefits of Trees.
• Interpretability: tree structured presentation, easy to
conceptualize but gets crowded with large trees.
• Mixed Measurement Scales
– Nominal, ordinal, interval variables.
– Regression trees for continuous target variable.
• Robustnes. Outliers just become additional possible split value.
• Missing Values: treated as one more possible split value.
• Automatic variable selection, and even ‘coefficients’ (I.e.,
splitting points) because splitter can be undrstood as selected variable,
but not in the linear model sense.
16. Leonardo Auslender Copyright 2004 Ch. 1.4-16
10/3/2019
…Benefits.
• Automatically
– Detects interactions
(AID) in hierarchical
conditioning search, i.e.,
hierarchy level is all
important.
– Invariance under
monotonic
transformations. All that
matters is values rankings.
Input
Input
Prob
Multivariate
Step Function
17. Leonardo Auslender Copyright 2004 Ch. 1.4-17
10/3/2019
Drawbacks of Trees.
• Unstable: small perturbations in data can lead to big changes in
trees, because splitting points can change.
• Linear structures are approximated in
very rough form.
• Applications may require that rules
descriptions for different categories not
share the same attributes (e.g., finance, splitters
may appear just once).
18. Leonardo Auslender Copyright 2004 Ch. 1.4-18
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Drawbacks of Trees (cont.).
• . Tend to over-fit ➔ overly optimistic accuracy (even when
pruned).
• . Large trees very difficult to interpret.
• . Tree size conditioned by data set size.
• . No valid inferential procedures at present (matters?).
• . Greedy search algorithm (one variable at a time, one step
ahead).
• . Difficulty in accepting final fit,
especially for data near boundaries.
• . Difficulties when data contains lot of missing values (but
other methods could be far worse in this case).
19. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 1.4-19
10/3/2019
/* PROGRAM ALGOR8.PGM WITH 8 FINAL NODES*/
/* METHOD MISSCL ALACART TEST */
RETAIN ROOT 1;
IF ROOT & CURRDUE <= 105.38 & PASTDUE <= 90.36 & CURRDUE <= 12
THEN DO;
NODE = '4_1 ';
PRED = 0 ;
/* % NODE IMPURITY = 0.0399 ; */
/* BRANCH # = 1 ; */
/* NODE FREQ = 81 ; */
END;
ELSE IF ROOT & CURRDUE <= 105.38 & PASTDUE <= 90.36 & CURRDUE > 12
THEN DO;
NODE = '4_2 ';
PRED = 1 ;
/* % NODE IMPURITY = 0.4478 ; */
/* BRANCH # = 2 ; */
/* NODE FREQ = 212 ; */
END;
ELSE IF ROOT & CURRDUE <= 105.38 & PASTDUE > 90.36
THEN DO;
NODE = '3_2 ';
PRED = 0 ;
Scoring Recipe: example of scoring output generated by TREE
like programs.
21. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 1.4-21
10/3/2019
With same data set, partial picture of Tree found, Example
with HMEQ data set..
23. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 1.4-23
10/3/2019
Tree Pruning.
Trained tree could be quite large and obtain seemingly low overall
misclassification rate due to over fitting. Pruning (Breiman’s et al,
1984) , aims at remedying fitting problem.
Starts from tree originally created and selectively recombines nodes
and obtains decreasing sequence of sub-trees from the bottom up.
Decision as to which final nodes to recombine depends on
comparing loss in accuracy from not splitting intermediate node
in relation to number of final nodes that that split generates.
Comparison made across all possible intermediate node splits, and
‘minimal cost-complexity’ loss in accuracy is rule for pruning.
Sequence of sub-trees generated ends up with root node. Decision
as to which tree among sub-trees to utilize is based on either one of
two methods: 1) cross-validation, or 2) a test-data set.
24. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 1.4-24
10/3/2019
Tree Pruning
1) Cross-validation.
Preferred when original data set is not ‘large’. ‘v’ stratified samples on
dependent variable are created, without replacement. Create ‘v’ data
sets, each one containing (v –1) of samples created, and ‘v’ test data
sets, which consists of ‘left-out’ sample. ‘v’ maximal trees are trained
on ‘v’ samples, and pruned.
For instance, let v = 10 and obtain 10 samples from original data set
without replacement. Then from the 10 samples, create 10 additional
data sets combining 9 of the 10 samples, and skipping a different one
each time. The left out sample is used as test data. Thus we obtain 10
training and 10 test samples. Create 10 maximal trees and prune them.
25. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 1.4-25
10/3/2019
Tree Pruning.
2) Test data set.
Test data set method preferred when size of data set is not
constraint on estimation process. Split original data set into training
and test subsets.
Once maximal tree and sequence of sub-trees due to pruning are
obtained, ‘score’ different sub-trees with test data set and obtain
the corresponding misclassification rates.
Choose that sub-tree which minimizes misclassification rate. While
this rate decreases with number of final nodes at stage of tree
development, it typically plateaus at some number of final nodes
smaller than maximal number of final nodes for test data set.
26. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 5-26
10/3/2019
Tree Pruning
The test data sets are then used to obtain misclassification rates of each
of pruning subsequences. Index each pruning subsequence and
corresponding misclassification rate by number of final nodes, and
obtain array of miscl. Rates by pruned-subtrees. Choose size of tree
that which minimizes overall misclassification rate.
Final tree will be taken from original pruning sequence of tree derived
with entire sample at number of final nodes just described.
28. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 5-28
10/3/2019
Variable Importance
Variable importance can be defined in many ways.
It can be considered as a measure of the actual splitting or the actual
and potential splitting capability of all variables.
By actual we mean variables that were used to create splits and by
potential we mean variables which mimic the primary splitter e.g.
surrogates. It involves calculating for each primary splitter and each
surrogate the improvement in the Gini or Entropy index or the chi-
square over all internal nodes weighted by the size of the node.
The final result is scaled so that the maximum value is 1.00.
l
i
j j
i = 1
N
Importance(x ) = improvement in Gini for variable x
N
44. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 5-44
10/3/2019
Precision + classification
Similar for VAL.
45. Leonardo Auslender Copyright 2004
Leonardo Auslender Ch. 5-45
10/3/2019
Comparing Gains-chart info with Precision Recall.
The gains-chart provides information on cumulative # of
Events per descending percentile / bin. These bins contain a
fixed number of observations.
Precision recall instead is at probability level, not at bin
Level, and thus # of observations along the curve is not
Uniform. Thus, selecting cutoff point from gains-chart selects
invariably from within a range of probabilities.
Selecting from Precision recall, selects a specific probability
point.
46. Leonardo Auslender Copyright 2004
Leonardo Auslender 46
10/3/2019
References
Auslender L. (1998): Alacart, poor man’s classification trees, NESUG.
Breiman L., Friedman J., Olshen R., Stone J. (1984): Classification and Regression Trees,
Wadsworth.
Chipman H., George E., McCulloch R.: BART, Bayesian additive regression Trees, The
Annals of Statistics.
Friedman, J. (2001).Greedy boosting approximation: a gradient boosting machine. Ann.Stat.
29, 1189–1232.doi:10.1214/aos/1013203451
Quinlan J. Ross (1993): C4.5: programs for machine learning, Morgan Kaufmann Publshers.