-
Notifications
You must be signed in to change notification settings - Fork 1
/
module_4_slides.html
397 lines (278 loc) · 9.05 KB
/
module_4_slides.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
<head>
<title>Module 4 More Analytics</title>
<meta charset="utf-8" />
<link rel="stylesheet" href="xaringan-themer.css" type="text/css" />
<link rel="stylesheet" href="other.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Module 4 <br><br> More Analytics
---
<style type="text/css">
.remark-slide-content {
font-size: 30px;
padding: 1em 4em 1em 4em;
}
</style>
## Extensions to standard models
Nonlinear relationships
- Generalized Additive Models
- Nonlinear models (e.g. `\(y \sim a\cdot e^{b\cdot x}\)`)
Unsupervised methods
- Clustering/PCA etc.
---
## Extensions to standard models
Text Analysis
- Sentiment
- Tagging
- Topic modeling
- Word prediction
Penalized Regression
- Lasso, Ridge, Elasticnet
---
## Machine Learning
<span class="emph">Machine learning</span> is an **approach** to data analysis
- Focus is almost exclusively on prediction
- Performance is assessed on **new** data
Any method can be used for machine learning
Performance greater for ML, but interepretation, if even desired, can be more challenging
---
## Machine Learning
Standard methods
- Penalized regression (a starting point)
- Random forests and boosted trees (e.g. XGBoost)
- Neural nets
- Large scale graphical modeling (e.g. networks)
Standard techniques
- Data pre-processing
- Parameter tuning
- Cross-validation
- Combining models
---
## Machine Learning
A hypothetical decision tree
This would obviously misclassify many
<img src="img/tree.png" style="display:block; margin: 0 auto;" width=75%>
---
## Machine Learning
However, if we had a thousand trees...
... we could base our final results on the total output
- e.g. average (regression) or most common (classification) prediction
---
## Machine Learning
Popular R machine learning frameworks
- <span class="pack">caret</span>
- <span class="pack">mlr</span>
---
## Machine Learning
Data is typically homogenized to have similar inputs for all columns
```r
library(caret)
set.seed(1234) # so that the indices will be the same when re-run
trainIndices = createDataPartition(model_variables$libuser, p=.8, list=F)
X_train = model_variables %>%
slice(trainIndices)
X_test = model_variables %>%
slice(-trainIndices)
```
---
## Machine Learning
Example with XGBoost
```r
library(xgboost)
xgb_opts = expand.grid(
eta = c(.3, .4),
max_depth = c(9, 12),
colsample_bytree = c(.6, .8),
subsample = c(.5, .75, 1),
nrounds = 100, # 1000 would be more reasonable, but notably time consuming
min_child_weight = 1,
gamma = 0
)
cv_opts = trainControl(method='cv', number=10)
```
---
## Machine Learning
```r
# for parallel processing
library(doParallel)
cl = makeCluster(detectCores() - 1)
registerDoParallel(cl)
results_xgb = train(
libuser ~ .,
data = X_train,
method = 'xgbTree',
preProcess = c('center', 'scale'),
trControl = cv_opts,
tuneGrid = xgb_opts
)
stopCluster(cl)
results_xgb
```
---
## Machine Learning
```r
preds_gb = predict(results_xgb, X_test)
confusionMatrix(preds_gb, X_test$libuser, positive='yes')
```
```
Confusion Matrix and Statistics
Reference
Prediction no yes
no 3276 2271
yes 1726 2727
Accuracy : 0.6003
95% CI : (0.5906, 0.6099)
No Information Rate : 0.5002
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.2006
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.5456
Specificity : 0.6549
Pos Pred Value : 0.6124
Neg Pred Value : 0.5906
Prevalence : 0.4998
Detection Rate : 0.2727
Detection Prevalence : 0.4453
Balanced Accuracy : 0.6003
'Positive' Class : yes
```
---
## Machine Learning
Machine learning will take time to do well
- Need a baseline as a comparison
Payoff may be minimal compared to well-done standard methods that can handle data complexities
- Simply getting a good result is not enough
Many approaches are actually not very good with big data out of the box
---
## Deep Learning
Neural networks form the basis of <span class="emph">deep learning</span>
- <span class="emph">AI</span> refers to specific applications of of machine learning that employ <span class="emph">deep learning</span> techniques
<img src="img/nnet.png" style="display:block; margin: 0 auto;" width=50%>
---
## Deep Learning
With deep learning, there may be dozens of hidden layers with possibly hundreds of nodes and additional complexities
Requires GPU for reasonable times
Typically work best for a specific task with consistent data type
Common frameworks include <span class="pack">tensorflow</span>, <span class="pack">pytorch</span>, etc.
Common wrappers include <span class="pack">keras</span>, <span class="pack">fastai</span>, <span class="pack">scikit-learn</span>, etc.
---
## Network Analysis
.pull-left[
Graphs
- nodes and edges
- directed
- undirected
- bipartite
- weighted
Networks
- Social, biological, etc.
]
.pull-right[
<span style="font-size: 20%;">image courtesy of Wikipedia</span>
]
---
## Network Analysis
Description
- Degree, betweeness, centrality, pagerank
Clusters
- Community detection algorithms
Models
- Exponential random graph
- Bayesian networks
---
## Network Analysis
Demo
`network_analysis.R`
</textarea>
<style data-target="print-only">@media screen {.remark-slide-container{display:block;}.remark-slide-scaler{box-shadow:none;}}</style>
<script src="https://remarkjs.com/downloads/remark-latest.min.js"></script>
<script>var slideshow = remark.create({
"highlightStyle": "github",
"highlightLines": true,
"countIncrementalSlides": false
});
if (window.HTMLWidgets) slideshow.on('afterShowSlide', function (slide) {
window.dispatchEvent(new Event('resize'));
});
(function(d) {
var s = d.createElement("style"), r = d.querySelector(".remark-slide-scaler");
if (!r) return;
s.type = "text/css"; s.innerHTML = "@page {size: " + r.style.width + " " + r.style.height +"; }";
d.head.appendChild(s);
})(document);
(function(d) {
var el = d.getElementsByClassName("remark-slides-area");
if (!el) return;
var slide, slides = slideshow.getSlides(), els = el[0].children;
for (var i = 1; i < slides.length; i++) {
slide = slides[i];
if (slide.properties.continued === "true" || slide.properties.count === "false") {
els[i - 1].className += ' has-continuation';
}
}
var s = d.createElement("style");
s.type = "text/css"; s.innerHTML = "@media print { .has-continuation { display: none; } }";
d.head.appendChild(s);
})(document);
// delete the temporary CSS (for displaying all slides initially) when the user
// starts to view slides
(function() {
var deleted = false;
slideshow.on('beforeShowSlide', function(slide) {
if (deleted) return;
var sheets = document.styleSheets, node;
for (var i = 0; i < sheets.length; i++) {
node = sheets[i].ownerNode;
if (node.dataset["target"] !== "print-only") continue;
node.parentNode.removeChild(node);
}
deleted = true;
});
})();</script>
<script>
(function() {
var links = document.getElementsByTagName('a');
for (var i = 0; i < links.length; i++) {
if (/^(https?:)?\/\//.test(links[i].getAttribute('href'))) {
links[i].target = '_blank';
}
}
})();
</script>
<script>
slideshow._releaseMath = function(el) {
var i, text, code, codes = el.getElementsByTagName('code');
for (i = 0; i < codes.length;) {
code = codes[i];
if (code.parentNode.tagName !== 'PRE' && code.childElementCount === 0) {
text = code.textContent;
if (/^\\\((.|\s)+\\\)$/.test(text) || /^\\\[(.|\s)+\\\]$/.test(text) ||
/^\$\$(.|\s)+\$\$$/.test(text) ||
/^\\begin\{([^}]+)\}(.|\s)+\\end\{[^}]+\}$/.test(text)) {
code.outerHTML = code.innerHTML; // remove <code></code>
continue;
}
}
i++;
}
};
slideshow._releaseMath(document);
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement('script');
script.type = 'text/javascript';
script.src = 'https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-MML-AM_CHTML';
if (location.protocol !== 'file:' && /^https?:/.test(script.src))
script.src = script.src.replace(/^https?:/, '');
document.getElementsByTagName('head')[0].appendChild(script);
})();
</script>
</body>
</html>