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danhalligan committed Aug 27, 2024
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10 changes: 5 additions & 5 deletions 03-linear-regression.md
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2 changes: 1 addition & 1 deletion 05-resampling-methods.md
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Expand Up @@ -170,7 +170,7 @@ mean(store)
```
```
## [1] 0.6308
## [1] 0.6355
```

The probability of including $4$ when resampling numbers $1...100$ is close to
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4 changes: 2 additions & 2 deletions 08-tree-based-methods.md
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Expand Up @@ -509,7 +509,7 @@ bartfit <- gbart(Carseats[train, 2:11], Carseats[train, 1],
## done 800 (out of 1100)
## done 900 (out of 1100)
## done 1000 (out of 1100)
## time: 2s
## time: 3s
## trcnt,tecnt: 1000,1000
```

Expand Down Expand Up @@ -1150,7 +1150,7 @@ bart <- gbart(College[train, pred], College[train, "Outstate"],
## done 800 (out of 1100)
## done 900 (out of 1100)
## done 1000 (out of 1100)
## time: 4s
## time: 3s
## trcnt,tecnt: 1000,1000
```

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68 changes: 34 additions & 34 deletions 10-deep-learning.md
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Expand Up @@ -393,15 +393,15 @@ npred <- predict(nn, x[testid, ])
```

```
## 6/6 - 0s - 61ms/epoch - 10ms/step
## 6/6 - 0s - 55ms/epoch - 9ms/step
```

``` r
mean(abs(y[testid] - npred))
```

```
## [1] 2.219039
## [1] 2.269432
```

In this case, the neural network outperforms logistic regression having a lower
Expand Down Expand Up @@ -433,7 +433,7 @@ model <- application_resnet50(weights = "imagenet")

```
## Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5
## 8192/102967424 [..............................] - ETA: 0s 3956736/102967424 [>.............................] - ETA: 1s 4202496/102967424 [>.............................] - ETA: 2s 8396800/102967424 [=>............................] - ETA: 1s 16785408/102967424 [===>..........................] - ETA: 1s 25174016/102967424 [======>.......................] - ETA: 1s 33562624/102967424 [========>.....................] - ETA: 0s 41951232/102967424 [===========>..................] - ETA: 0s 50905088/102967424 [=============>................] - ETA: 0s 58728448/102967424 [================>.............] - ETA: 0s 67117056/102967424 [==================>...........] - ETA: 0s 83894272/102967424 [=======================>......] - ETA: 0s101908480/102967424 [============================>.] - ETA: 0s102967424/102967424 [==============================] - 1s 0us/step
## 8192/102967424 [..............................] - ETA: 0s 4202496/102967424 [>.............................] - ETA: 3s 14876672/102967424 [===>..........................] - ETA: 1s 16785408/102967424 [===>..........................] - ETA: 1s 25174016/102967424 [======>.......................] - ETA: 1s 33562624/102967424 [========>.....................] - ETA: 1s 41951232/102967424 [===========>..................] - ETA: 1s 53223424/102967424 [==============>...............] - ETA: 0s 58728448/102967424 [================>.............] - ETA: 0s 68878336/102967424 [===================>..........] - ETA: 0s 75505664/102967424 [====================>.........] - ETA: 0s 86802432/102967424 [========================>.....] - ETA: 0s 92282880/102967424 [=========================>....] - ETA: 0s102967424/102967424 [==============================] - 1s 0us/step
```

``` r
Expand Down Expand Up @@ -721,15 +721,15 @@ kpred <- predict(model, xrnn[!istrain,, ])
```

```
## 56/56 - 0s - 58ms/epoch - 1ms/step
## 56/56 - 0s - 59ms/epoch - 1ms/step
```

``` r
1 - mean((kpred - arframe[!istrain, "log_volume"])^2) / V0
```

```
## [1] 0.412886
## [1] 0.4129694
```

Both models estimate the same number of coefficients/weights (16):
Expand Down Expand Up @@ -763,24 +763,24 @@ model$get_weights()
```
## [[1]]
## [,1]
## [1,] -0.031145222
## [2,] 0.101065643
## [3,] 0.141815767
## [4,] -0.004181504
## [5,] 0.116010934
## [6,] -0.003764492
## [7,] 0.038601257
## [8,] 0.078083567
## [9,] 0.137415737
## [10,] -0.029184511
## [11,] 0.036070298
## [12,] -0.821708620
## [13,] 0.095548652
## [14,] 0.511229098
## [15,] 0.521453559
## [1,] -0.032474127
## [2,] 0.097779043
## [3,] 0.178456694
## [4,] -0.005626136
## [5,] 0.121273242
## [6,] -0.076247886
## [7,] 0.035232600
## [8,] 0.077857092
## [9,] 0.163645267
## [10,] -0.026966114
## [11,] 0.032263778
## [12,] -0.807968795
## [13,] 0.095888853
## [14,] 0.513532162
## [15,] 0.496699780
##
## [[2]]
## [1] -0.006889343
## [1] -0.004996791
```

The flattened RNN has a lower $R^2$ on the test data than our `lm` model
Expand Down Expand Up @@ -833,11 +833,11 @@ xfun::cache_rds({
```

```
## 56/56 - 0s - 66ms/epoch - 1ms/step
## 56/56 - 0s - 64ms/epoch - 1ms/step
```

```
## [1] 0.4271516
## [1] 0.4262716
```

This approach improves our $R^2$ over the linear model above.
Expand Down Expand Up @@ -906,11 +906,11 @@ xfun::cache_rds({
```

```
## 56/56 - 0s - 133ms/epoch - 2ms/step
## 56/56 - 0s - 134ms/epoch - 2ms/step
```

```
## [1] 0.4405331
## [1] 0.4429825
```

### Question 13
Expand Down Expand Up @@ -966,21 +966,21 @@ xfun::cache_rds({

```
## Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz
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## 782/782 - 16s - 16s/epoch - 20ms/step
## 782/782 - 16s - 16s/epoch - 20ms/step
## 782/782 - 16s - 16s/epoch - 20ms/step
## 782/782 - 16s - 16s/epoch - 20ms/step
## 8192/17464789 [..............................] - ETA: 0s 2924544/17464789 [====>.........................] - ETA: 0s 4202496/17464789 [======>.......................] - ETA: 0s 8396800/17464789 [=============>................] - ETA: 0s17464789/17464789 [==============================] - 0s 0us/step
## 782/782 - 15s - 15s/epoch - 19ms/step
## 782/782 - 15s - 15s/epoch - 20ms/step
## 782/782 - 15s - 15s/epoch - 20ms/step
## 782/782 - 15s - 15s/epoch - 20ms/step
```



| Max Features| Accuracy|
|------------:|--------:|
| 1000| 0.86084|
| 3000| 0.87224|
| 5000| 0.87460|
| 10000| 0.86180|
| 1000| 0.85324|
| 3000| 0.87808|
| 5000| 0.88076|
| 10000| 0.86936|

Varying the dictionary size does not make a substantial impact on our estimates
of accuracy. However, the models do take a substantial amount of time to fit and
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