diff --git a/pull313/_sources/classification2.md b/pull313/_sources/classification2.md index bc250318..8b387a81 100644 --- a/pull313/_sources/classification2.md +++ b/pull313/_sources/classification2.md @@ -280,7 +280,7 @@ we call the `seed` function from the `numpy` package, and pass it any integer as Below we use the seed number `1`. At that point, Python will keep track of the randomness that occurs throughout the code. For example, we can call the `sample` method -on the series of numbers, passing the argument `n = 10` to indicate that we want 10 samples. +on the series of numbers, passing the argument `n=10` to indicate that we want 10 samples. ```{code-cell} ipython3 import numpy as np @@ -290,7 +290,7 @@ np.random.seed(1) nums_0_to_9 = pd.Series([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) -random_numbers1 = nums_0_to_9.sample(n = 10).to_numpy() +random_numbers1 = nums_0_to_9.sample(n=10).to_numpy() random_numbers1 ``` You can see that `random_numbers1` is a list of 10 numbers @@ -299,7 +299,7 @@ we run the `sample` method again, we will get a fresh batch of 10 numbers that also look random. ```{code-cell} ipython3 -random_numbers2 = nums_0_to_9.sample(n = 10).to_numpy() +random_numbers2 = nums_0_to_9.sample(n=10).to_numpy() random_numbers2 ``` @@ -309,12 +309,12 @@ as before---and then call the `sample` method again. ```{code-cell} ipython3 np.random.seed(1) -random_numbers1_again = nums_0_to_9.sample(n = 10).to_numpy() +random_numbers1_again = nums_0_to_9.sample(n=10).to_numpy() random_numbers1_again ``` ```{code-cell} ipython3 -random_numbers2_again = nums_0_to_9.sample(n = 10).to_numpy() +random_numbers2_again = nums_0_to_9.sample(n=10).to_numpy() random_numbers2_again ``` @@ -326,12 +326,12 @@ obtain a different sequence of random numbers. ```{code-cell} ipython3 np.random.seed(4235) -random_numbers = nums_0_to_9.sample(n = 10).to_numpy() +random_numbers = nums_0_to_9.sample(n=10).to_numpy() random_numbers ``` ```{code-cell} ipython3 -random_numbers = nums_0_to_9.sample(n = 10).to_numpy() +random_numbers = nums_0_to_9.sample(n=10).to_numpy() random_numbers ``` @@ -378,15 +378,15 @@ functions. Those functions will then use your `RandomState` to generate random n `numpy`'s default generator. For example, we can reproduce our earlier example by using a `RandomState` object with the `seed` value set to 1; we get the same lists of numbers once again. ```{code} -rnd = np.random.RandomState(seed = 1) -random_numbers1_third = nums_0_to_9.sample(n = 10, random_state = rnd).to_numpy() +rnd = np.random.RandomState(seed=1) +random_numbers1_third = nums_0_to_9.sample(n=10, random_state=rnd).to_numpy() random_numbers1_third ``` ```{code} array([2, 9, 6, 4, 0, 3, 1, 7, 8, 5]) ``` ```{code} -random_numbers2_third = nums_0_to_9.sample(n = 10, random_state = rnd).to_numpy() +random_numbers2_third = nums_0_to_9.sample(n=10, random_state=rnd).to_numpy() random_numbers2_third ``` ```{code} @@ -540,8 +540,8 @@ cancer_train["Class"].value_counts(normalize=True) ```{code-cell} ipython3 :tags: [remove-cell] -glue("cancer_train_b_prop", "{:0.0f}".format(cancer_train["Class"].value_counts(normalize = True)["Benign"]*100)) -glue("cancer_train_m_prop", "{:0.0f}".format(cancer_train["Class"].value_counts(normalize = True)["Malignant"]*100)) +glue("cancer_train_b_prop", "{:0.0f}".format(cancer_train["Class"].value_counts(normalize=True)["Benign"]*100)) +glue("cancer_train_m_prop", "{:0.0f}".format(cancer_train["Class"].value_counts(normalize=True)["Malignant"]*100)) ``` ### Preprocess the data @@ -1620,7 +1620,7 @@ for i in range(len(ks)): cancer_tune_pipe = make_pipeline(cancer_preprocessor, KNeighborsClassifier()) param_grid = { "kneighborsclassifier__n_neighbors": range(1, 21), - } ## double check: in R textbook, it is tune_grid(..., grid = 20), so I guess it matches RandomizedSearchCV + } ## double check: in R textbook, it is tune_grid(..., grid=20), so I guess it matches RandomizedSearchCV ## instead of GridSeachCV? # param_grid_rand = { # "kneighborsclassifier__n_neighbors": range(1, 100), diff --git a/pull313/_sources/clustering.md b/pull313/_sources/clustering.md index 25de3c0c..dc1c6759 100644 --- a/pull313/_sources/clustering.md +++ b/pull313/_sources/clustering.md @@ -182,10 +182,10 @@ in the clustering pipeline. ```{code-cell} ipython3 :tags: [remove-cell] penguins_standardized = penguins.assign( - bill_length_standardized = (penguins["bill_length_mm"] - penguins["bill_length_mm"].mean())/penguins["bill_length_mm"].std(), - flipper_length_standardized = (penguins["flipper_length_mm"] - penguins["flipper_length_mm"].mean())/penguins["flipper_length_mm"].std() + bill_length_standardized=(penguins["bill_length_mm"] - penguins["bill_length_mm"].mean())/penguins["bill_length_mm"].std(), + flipper_length_standardized=(penguins["flipper_length_mm"] - penguins["flipper_length_mm"].mean())/penguins["flipper_length_mm"].std() ).drop( - columns = ["bill_length_mm", "flipper_length_mm"] + columns=["bill_length_mm", "flipper_length_mm"] ) ``` @@ -261,7 +261,7 @@ kmeans = KMeans(n_clusters=3) penguin_clust = kmeans.fit(penguins_standardized) -penguins_clustered = penguins_standardized.assign(cluster = penguin_clust.labels_) +penguins_clustered = penguins_standardized.assign(cluster=penguin_clust.labels_) colored_scatter_plot = alt.Chart(penguins_clustered).mark_circle().encode( x=alt.X("flipper_length_standardized", title="Flipper Length (standardized)"), diff --git a/pull313/_sources/inference.md b/pull313/_sources/inference.md index bdb9d4b7..6e89cc1d 100644 --- a/pull313/_sources/inference.md +++ b/pull313/_sources/inference.md @@ -716,9 +716,9 @@ glue( x="mean(price)" ), base.mark_text(align="left", color="#f58518", size=12, fontWeight="bold", dx=10).transform_aggregate( - mean_price = "mean(price)", + mean_price="mean(price)", ).transform_calculate( - label = "'Mean = ' + round(datum.mean_price * 10) / 10" + label="'Mean = ' + round(datum.mean_price * 10) / 10" ).encode( x=alt.X("mean_price:Q", title="Sample mean price per night (dollars)"), y=alt.value(10), diff --git a/pull313/_sources/regression1.md b/pull313/_sources/regression1.md index e20fe67c..973d14bd 100644 --- a/pull313/_sources/regression1.md +++ b/pull313/_sources/regression1.md @@ -833,8 +833,8 @@ from sklearn.metrics import mean_squared_error sacramento_test["predicted"] = sacr_gridsearch.predict(sacramento_test) RMSPE = mean_squared_error( - y_true = sacramento_test["price"], - y_pred = sacramento_test["predicted"] + y_true=sacramento_test["price"], + y_pred=sacramento_test["predicted"] )**(1/2) RMSPE ``` @@ -1066,8 +1066,8 @@ to compute the RMSPE. ```{code-cell} ipython3 sacramento_test["predicted"] = sacr_gridsearch.predict(sacramento_test) RMSPE_mult = mean_squared_error( - y_true = sacramento_test["price"], - y_pred = sacramento_test["predicted"] + y_true=sacramento_test["price"], + y_pred=sacramento_test["predicted"] )**(1/2) RMSPE_mult diff --git a/pull313/_sources/regression2.md b/pull313/_sources/regression2.md index edca8052..60db7cd4 100644 --- a/pull313/_sources/regression2.md +++ b/pull313/_sources/regression2.md @@ -440,8 +440,8 @@ sacramento_test["predicted"] = lm.predict(sacramento_test[["sqft"]]) # calculate RMSPE RMSPE = mean_squared_error( - y_true = sacramento_test["price"], - y_pred = sacramento_test["predicted"] + y_true=sacramento_test["price"], + y_pred=sacramento_test["predicted"] )**(1/2) RMSPE @@ -734,8 +734,8 @@ Finally, we make predictions on the test data set to assess the quality of our m sacramento_test["predicted"] = mlm.predict(sacramento_test[["sqft","beds"]]) lm_mult_test_RMSPE = mean_squared_error( - y_true = sacramento_test["price"], - y_pred = sacramento_test["predicted"] + y_true=sacramento_test["price"], + y_pred=sacramento_test["predicted"] )**(1/2) lm_mult_test_RMSPE ``` diff --git a/pull313/classification1.html b/pull313/classification1.html index 857dc6fc..e21b08de 100644 --- a/pull313/classification1.html +++ b/pull313/classification1.html @@ -872,23 +872,23 @@

5.4.3. Exploring the cancer data
-
+
@@ -982,23 +982,23 @@

5.5. Classification with
-
+

Fig. 5.2 Scatter plot of concavity versus perimeter with new observation represented as a red diamond.#

@@ -1057,23 +1057,23 @@

5.5. Classification with
-
+

Fig. 5.3 Scatter plot of concavity versus perimeter. The new observation is represented as a red diamond with a line to the one nearest neighbor, which has a malignant @@ -1136,23 +1136,23 @@

5.5. Classification with
-
+

Fig. 5.4 Scatter plot of concavity versus perimeter. The new observation is represented as a red diamond with a line to the one nearest neighbor, which has a benign @@ -1215,23 +1215,23 @@

5.5. Classification with
-
+

Fig. 5.5 Scatter plot of concavity versus perimeter with three nearest neighbors.#

@@ -1311,23 +1311,23 @@

5.5.1. Distance between points
-
+

Fig. 5.6 Scatter plot of concavity versus perimeter with new observation represented as a red diamond.#

@@ -1507,23 +1507,23 @@

5.5.1. Distance between points
-
+

Fig. 5.7 Scatter plot of concavity versus perimeter with 5 nearest neighbors circled.#

@@ -1719,9 +1719,9 @@

5.5.2. More than two explanatory variabl }); } -

Fig. 5.9 Comparison of K = 3 nearest neighbors with standardized and unstandardized data.#

@@ -2512,23 +2512,23 @@

5.7.1. Centering and scaling
-
+

Fig. 5.10 Close-up of three nearest neighbors for unstandardized data.#

@@ -2625,23 +2625,23 @@

5.7.2. Balancing
-
+

@@ -2722,23 +2722,23 @@

5.7.2. Balancing
-
+

Fig. 5.12 Imbalanced data with 7 nearest neighbors to a new observation highlighted.#

@@ -2796,23 +2796,23 @@

5.7.2. Balancing
-
+

Fig. 5.13 Imbalanced data with background color indicating the decision of the classifier and the points represent the labeled data.#

@@ -2906,23 +2906,23 @@

5.7.2. Balancing
-
+

Fig. 5.14 Upsampled data with background color indicating the decision of the classifier.#

@@ -3445,23 +3445,23 @@

5.7.3. Missing data
-
+
diff --git a/pull313/classification2.html b/pull313/classification2.html index 31f0d309..34749c61 100644 --- a/pull313/classification2.html +++ b/pull313/classification2.html @@ -599,7 +599,7 @@

6.3. Evaluating performance1. At that point, Python will keep track of the randomness that occurs throughout the code. For example, we can call the sample method -on the series of numbers, passing the argument n = 10 to indicate that we want 10 samples.

+on the series of numbers, passing the argument n=10 to indicate that we want 10 samples.

import numpy as np
@@ -609,7 +609,7 @@ 
6.3. Evaluating performancenums_0_to_9 = pd.Series([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
 
-random_numbers1 = nums_0_to_9.sample(n = 10).to_numpy()
+random_numbers1 = nums_0_to_9.sample(n=10).to_numpy()
 random_numbers1
@@ -626,7 +626,7 @@

6.3. Evaluating performance
-
random_numbers2 = nums_0_to_9.sample(n = 10).to_numpy()
+
random_numbers2 = nums_0_to_9.sample(n=10).to_numpy()
 random_numbers2
 

 

@@ -643,7 +643,7 @@ 
6.3. Evaluating performance
 

 
np.random.seed(1)
-random_numbers1_again = nums_0_to_9.sample(n = 10).to_numpy()
+random_numbers1_again = nums_0_to_9.sample(n=10).to_numpy()
 random_numbers1_again
 

 

@@ -656,7 +656,7 @@ 
6.3. Evaluating performance
 

-
random_numbers2_again = nums_0_to_9.sample(n = 10).to_numpy()
+
random_numbers2_again = nums_0_to_9.sample(n=10).to_numpy()
 random_numbers2_again
 

 

@@ -675,7 +675,7 @@ 
6.3. Evaluating performance
 

 
np.random.seed(4235)
-random_numbers = nums_0_to_9.sample(n = 10).to_numpy()
+random_numbers = nums_0_to_9.sample(n=10).to_numpy()
 random_numbers
 

 

@@ -688,7 +688,7 @@ 
6.3. Evaluating performance
 

-
random_numbers = nums_0_to_9.sample(n = 10).to_numpy()
+
random_numbers = nums_0_to_9.sample(n=10).to_numpy()
 random_numbers
 

 

@@ -738,15 +738,15 @@ 
6.3. Evaluating performanceRandomState to generate random numbers instead of
 numpy’s default generator. For example, we can reproduce our earlier example by using a RandomState
 object with the seed value set to 1; we get the same lists of numbers once again.

-
rnd = np.random.RandomState(seed = 1)
-random_numbers1_third = nums_0_to_9.sample(n = 10, random_state = rnd).to_numpy()
+
rnd = np.random.RandomState(seed=1)
+random_numbers1_third = nums_0_to_9.sample(n=10, random_state=rnd).to_numpy()
 random_numbers1_third
 

 

 
array([2, 9, 6, 4, 0, 3, 1, 7, 8, 5])
 

 

-
random_numbers2_third = nums_0_to_9.sample(n = 10, random_state = rnd).to_numpy()
+
random_numbers2_third = nums_0_to_9.sample(n=10, random_state=rnd).to_numpy()
 random_numbers2_third
 

 

@@ -804,23 +804,23 @@ 
6.5. Evaluating performance with 
 

 
-

+

 

 

 

@@ -1523,32 +1523,32 @@ 
6.6.1. Cross-validation6.6.1. Cross-validation6.6.1. Cross-validation6.6.1. Cross-validation6.6.2. Parameter value selection
 

 
-

+

 

 
Fig. 6.5 Plot of estimated accuracy versus the number of neighbors.#

 

@@ -2256,23 +2256,23 @@ 
6.6.3. Under/Overfitting
 

 
-

+

 

 
Fig. 6.6 Plot of accuracy estimate versus number of neighbors for many K values.#

 

@@ -2347,23 +2347,23 @@ 
6.6.3. Under/Overfitting
 

 
-

+

 

 
Fig. 6.7 Effect of K in overfitting and underfitting.#

 

@@ -2648,23 +2648,23 @@ 
6.8.1. The effect of irrelevant predicto
 

 

 
-

+

 

 
Fig. 6.9 Effect of inclusion of irrelevant predictors.#

 

@@ -2727,23 +2727,23 @@ 
6.8.1. The effect of irrelevant predicto
 

 

 
-

+

 

 
Fig. 6.10 Tuned number of neighbors for varying number of irrelevant predictors.#

 

@@ -2797,23 +2797,23 @@ 
6.8.1. The effect of irrelevant predicto
 

 

 
-

+

 

 
Fig. 6.11 Accuracy versus number of irrelevant predictors for tuned and untuned number of neighbors.#

 

@@ -3276,23 +3276,23 @@ 
6.8.3. Forward selection in 
 

 
-

+

 

 
Fig. 6.12 Estimated accuracy versus the number of predictors for the sequence of models built using forward selection.#

 

diff --git a/pull313/clustering.html b/pull313/clustering.html
index 881bd9cc..1c84d9c1 100644
--- a/pull313/clustering.html
+++ b/pull313/clustering.html
@@ -756,23 +756,23 @@ 
9.4. An illustrative example
 

 
-

+

 

 
Fig. 9.2 Scatter plot of standardized bill length versus standardized flipper length.#

 

@@ -850,23 +850,23 @@ 
9.4. An illustrative example
 

 
-

+

 

 
Fig. 9.3 Scatter plot of standardized bill length versus standardized flipper length with colored groups.#

 

@@ -959,23 +959,23 @@ 
9.5.1. Measuring cluster quality
 

 
-

+

 

 
Fig. 9.4 Cluster 0 from the penguins_standardized data set example. Observations are in blue, with the cluster center highlighted in orange.#

 

@@ -1042,23 +1042,23 @@ 
9.5.1. Measuring cluster quality
 

 
-

+

 

 
Fig. 9.5 Cluster 0 from the penguins_standardized data set example. Observations are in blue, with the cluster center highlighted in orange. The distances from the observations to the cluster center are represented as black lines.#

 

@@ -1121,23 +1121,23 @@ 
9.5.1. Measuring cluster quality
 

 
-

+

 

 
Fig. 9.6 All clusters from the penguins_standardized data set example. Observations are in blue, orange, and red with the cluster center highlighted in orange. The distances from the observations to each of the respective cluster centers are represented as black lines.#

 

@@ -1205,23 +1205,23 @@ 
9.5.2. The clustering algorithm
 

 
-

+

 

 
Fig. 9.7 Random initialization of labels.
 Each cluster is depicted as a different color and shape.#

@@ -1287,23 +1287,23 @@ 
9.5.2. The clustering algorithm
 

 
-

+

 

 
Fig. 9.8 First three iterations of K-means clustering on the penguins_standardized example data set. Each pair of plots corresponds to an iteration. Within the pair, the first plot depicts the center update, and the second plot depicts the reassignment of data to clusters. Cluster centers are indicated by larger points that are outlined in black.#

 

@@ -1373,23 +1373,23 @@ 
9.5.3. Random restarts
 

 
-

+

 

 
Fig. 9.9 Random initialization of labels.#

 

@@ -1444,23 +1444,23 @@ 
9.5.3. Random restarts
 

 
-

+

 

 
Fig. 9.10 First four iterations of K-means clustering on the penguins_standardized example data set with a poor random initialization. Each pair of plots corresponds to an iteration. Within the pair, the first plot depicts the center update, and the second plot depicts the reassignment of data to clusters. Cluster centers are indicated by larger points that are outlined in black.#

 

@@ -1530,23 +1530,23 @@ 
9.5.4. Choosing K
 

 
-

+

 

 
Fig. 9.11 Clustering of the penguin data for K clusters ranging from 1 to 9. Cluster centers are indicated by larger points that are outlined in black.#

 

@@ -1606,23 +1606,23 @@ 
9.5.4. Choosing K
 

 
-

+

 

 
Fig. 9.12 Total WSSD for K clusters ranging from 1 to 9.#

 

@@ -1934,23 +1934,23 @@ 
9.6. K-means in Python
 

 
-

+

 

 
Fig. 9.13 The data colored by the cluster assignments returned by K-means.#

 

@@ -2179,23 +2179,23 @@ 
9.6. K-means in Python
 

 
-

+

 

 
Fig. 9.14 A plot showing the total WSSD versus the number of clusters.#

 

diff --git a/pull313/inference.html b/pull313/inference.html
index 41c5be6f..5b7abb7d 100644
--- a/pull313/inference.html
+++ b/pull313/inference.html
@@ -1225,23 +1225,23 @@ 
10.4.1. Sampling distributions for propo
 

 

 
-

+

 

 
Fig. 10.2 Sampling distribution of the sample proportion for sample size 40.#

 

@@ -1349,23 +1349,23 @@ 
10.4.2. Sampling distributions for means
 

 

 
-

+

 

 
Fig. 10.3 Population distribution of price per night (dollars) for all Airbnb listings in Vancouver, Canada.#

 

@@ -1471,23 +1471,23 @@ 
10.4.2. Sampling distributions for means
 

 

 
-

+

 

 
Fig. 10.4 Distribution of price per night (dollars) for sample of 40 Airbnb listings.#

 

@@ -1686,23 +1686,23 @@ 
10.4.2. Sampling distributions for means
 

 

 
-

+

 

 
Fig. 10.5 Sampling distribution of the sample means for sample size of 40.#

 

@@ -1782,23 +1782,23 @@ 
10.4.2. Sampling distributions for means
 

 

 
-

+

 

 

 

@@ -1864,23 +1864,23 @@ 
10.4.2. Sampling distributions for means
 

 

 
-

+

 

 

 

@@ -2017,23 +2017,23 @@ 
10.5.1. Overview
 

 
-

+

 

 
Fig. 10.8 Comparison of samples of different sizes from the population.#

 

@@ -2622,23 +2622,23 @@ 
10.5.2. Bootstrapping in Python
 

 
-

+

 

 

 

@@ -2722,23 +2722,23 @@ 
10.5.2. Bootstrapping in Python
 

 
-

+

 

 

 

@@ -3018,23 +3018,23 @@ 
10.5.2. Bootstrapping in Python
 

 
-

+

 

 

 

@@ -3291,23 +3291,23 @@ 
10.5.2. Bootstrapping in Python
 

 
-

+

 

 

 

@@ -3368,23 +3368,23 @@ 
10.5.2. Bootstrapping in Python
 

 
-

+
@@ -3544,23 +3544,23 @@

10.5.3. Using the bootstrap to calculate
-
+

diff --git a/pull313/intro.html b/pull313/intro.html index f7441397..1fdad888 100644 --- a/pull313/intro.html +++ b/pull313/intro.html @@ -2047,23 +2047,23 @@

1.11.1. Using
-
+

Fig. 1.8 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue#

@@ -2151,23 +2151,23 @@

1.11.1. Using
-
+

Fig. 1.9 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue with x and y labels. Note that this visualization is not done yet; there are still improvements to be made.#

@@ -2237,23 +2237,23 @@

1.11.1. Using
-
+

Fig. 1.10 Horizontal bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue. There are no more serious issues with this visualization, but it could be refined further.#

@@ -2325,23 +2325,23 @@

1.11.1. Using
-
+

Fig. 1.11 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue with bars reordered.#

@@ -2451,23 +2451,23 @@

1.11.3. Putting it all together
-
+

Fig. 1.12 Bar plot of the ten Aboriginal languages most often reported by Canadian residents as their mother tongue#

diff --git a/pull313/regression1.html b/pull313/regression1.html index 6e210d22..08de1fda 100644 --- a/pull313/regression1.html +++ b/pull313/regression1.html @@ -678,23 +678,23 @@

7.4. Exploring a data set
-
+

Fig. 7.1 Scatter plot of price (USD) versus house size (square feet).#

@@ -807,23 +807,23 @@

7.5. K-nearest neighbors regression
-
+

Fig. 7.2 Scatter plot of price (USD) versus house size (square feet) with vertical line indicating 2,000 square feet on x-axis.#

@@ -992,23 +992,23 @@

7.5. K-nearest neighbors regression
-
+

Fig. 7.3 Scatter plot of price (USD) versus house size (square feet) with lines to 5 nearest neighbors.#

@@ -1082,23 +1082,23 @@

7.5. K-nearest neighbors regression
-
+

Fig. 7.4 Scatter plot of price (USD) versus house size (square feet) with predicted price for a 2,000 square-foot house based on 5 nearest neighbors represented as a red dot.#

@@ -1221,23 +1221,23 @@

7.6. Training, evaluating, and tuning th
-
+

Fig. 7.5 Scatter plot of price (USD) versus house size (square feet) with example predictions (blue line) and the error in those predictions compared with true response values for three selected observations (vertical red lines).#

@@ -1612,23 +1612,23 @@

7.6. Training, evaluating, and tuning th
-
+

Fig. 7.6 Effect of the number of neighbors on the RMSPE.#

@@ -1710,23 +1710,23 @@

7.7. Underfitting and overfitting
-
+

Fig. 7.7 Predicted values for house price (represented as a orange line) from KNN regression models for six different values for \(K\).#

@@ -1839,8 +1839,8 @@

7.8. Evaluating on the test setsacramento_test["predicted"] = sacr_gridsearch.predict(sacramento_test) RMSPE = mean_squared_error( - y_true = sacramento_test["price"], - y_pred = sacramento_test["predicted"] + y_true=sacramento_test["price"], + y_pred=sacramento_test["predicted"] )**(1/2) RMSPE

@@ -1920,23 +1920,23 @@

7.8. Evaluating on the test set
-
+

Fig. 7.8 Predicted values of house price (orange line) for the final KNN regression model.#

@@ -2027,23 +2027,23 @@

7.9. Multivariable KNN regression
-
+

Fig. 7.9 Scatter plot of the sale price of houses versus the number of bedrooms.#

@@ -2207,8 +2207,8 @@

7.9. Multivariable KNN regression 
sacramento_test["predicted"] = sacr_gridsearch.predict(sacramento_test)
 RMSPE_mult = mean_squared_error(
-    y_true = sacramento_test["price"], 
-    y_pred = sacramento_test["predicted"]
+    y_true=sacramento_test["price"],
+    y_pred=sacramento_test["predicted"]
 )**(1/2)
 RMSPE_mult
@@ -2248,9 +2248,9 @@

7.9. Multivariable KNN regression -

Fig. 8.1 Scatter plot of sale price versus size with line of best fit for subset of the Sacramento housing data.#

@@ -529,23 +529,23 @@

8.3. Simple linear regression
-
+

Fig. 8.2 Scatter plot of sale price versus size with line of best fit and a red dot at the predicted sale price for a 2,000 square-foot home.#

@@ -605,23 +605,23 @@

8.3. Simple linear regression
-
+

Fig. 8.3 Scatter plot of sale price versus size with many possible lines that could be drawn through the data points.#

@@ -681,23 +681,23 @@

8.3. Simple linear regression
-
+

Fig. 8.4 Scatter plot of sale price versus size with red lines denoting the vertical distances between the predicted values and the observed data points.#

@@ -868,8 +868,8 @@

8.4. Linear regression in Python# calculate RMSPE RMSPE = mean_squared_error( - y_true = sacramento_test["price"], - y_pred = sacramento_test["predicted"] + y_true=sacramento_test["price"], + y_pred=sacramento_test["predicted"] )**(1/2) RMSPE @@ -927,23 +927,23 @@

8.4. Linear regression in Python
-
+

Fig. 8.5 Scatter plot of sale price versus size with line of best fit for the full Sacramento housing data.#

@@ -1006,23 +1006,23 @@

8.5. Comparing simple linear and KNN reg
-
+

Fig. 8.6 Comparison of simple linear regression and KNN regression.#

@@ -1154,8 +1154,8 @@

8.6. Multivariable linear regression
sacramento_test["predicted"] = mlm.predict(sacramento_test[["sqft","beds"]])
 
 lm_mult_test_RMSPE = mean_squared_error(
-    y_true = sacramento_test["price"], 
-    y_pred = sacramento_test["predicted"]
+    y_true=sacramento_test["price"],
+    y_pred=sacramento_test["predicted"]
 )**(1/2)
 lm_mult_test_RMSPE
@@ -1193,9 +1193,9 @@

8.6. Multivariable linear regression -

Fig. 8.8 Scatter plot of a subset of the data, with outlier highlighted in red.#

@@ -1420,23 +1420,23 @@

8.7.1. Outliers
-
+

Fig. 8.9 Scatter plot of the full data, with outlier highlighted in red.#

@@ -1502,23 +1502,23 @@

8.7.2. Multicollinearity
-
+

Fig. 8.10 Scatter plot of house size (in square feet) measured by person 1 versus house size (in square feet) measured by person 2.#

@@ -1691,23 +1691,23 @@

8.8. Designing new predictors
-
+

Fig. 8.11 Example of a data set with a nonlinear relationship between the predictor and the response.#

@@ -1779,23 +1779,23 @@

8.8. Designing new predictors
-
+

Fig. 8.12 Relationship between the transformed predictor and the response.#

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4.5.1. Scatter plots and line plots: the
-
+

Fig. 4.2 Scatter plot of atmospheric concentration of CO\(_{2}\) over time.#

@@ -839,23 +839,23 @@

4.5.1. Scatter plots and line plots: the
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Fig. 4.3 Line plot of atmospheric concentration of CO\(_{2}\) over time.#

@@ -935,23 +935,23 @@

4.5.1. Scatter plots and line plots: the
-
+

Fig. 4.4 Line plot of atmospheric concentration of CO\(_{2}\) over time with clearer axes and labels.#

@@ -1041,23 +1041,23 @@

4.5.1. Scatter plots and line plots: the
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+

Fig. 4.5 Line plot of atmospheric concentration of CO\(_{2}\) from 1990 to 1995.#

@@ -1253,23 +1253,23 @@

4.5.2. Scatter plots: the Old Faithful e
-
+

Fig. 4.6 Scatter plot of waiting time and eruption time.#

@@ -1340,23 +1340,23 @@

4.5.2. Scatter plots: the Old Faithful e
-
+

Fig. 4.7 Scatter plot of waiting time and eruption time with clearer axes and labels.#

@@ -1421,23 +1421,23 @@

4.5.2. Scatter plots: the Old Faithful e
-
+

Fig. 4.8 Scatter plot of waiting time and eruption time with black points.#

@@ -1663,23 +1663,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.9 Scatter plot of number of Canadians reporting a language as their mother tongue vs the primary language at home#

@@ -1757,23 +1757,23 @@

4.5.3. Axis transformation and colored s
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+

Fig. 4.10 Scatter plot of number of Canadians reporting a language as their mother tongue vs the primary language at home with x and y labels.#

@@ -1931,23 +1931,23 @@

4.5.3. Axis transformation and colored s
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+

Fig. 4.11 Scatter plot of number of Canadians reporting a language as their mother tongue vs the primary language at home with log-adjusted x and y axes.#

@@ -2027,23 +2027,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.12 Scatter plot of number of Canadians reporting a language as their mother tongue vs the primary language at home with log-adjusted x and y axes. Only the major gridlines are shown. The suffix “k” indicates 1,000 (“kilo”), while the suffix “M” indicates 1,000,000 (“million”).#

@@ -2234,23 +2234,23 @@

4.5.3. Axis transformation and colored s
-
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Fig. 4.13 Scatter plot of percentage of Canadians reporting a language as their mother tongue vs the primary language at home.#

@@ -2371,23 +2371,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.14 Scatter plot of percentage of Canadians reporting a language as their mother tongue vs the primary language at home colored by language category.#

@@ -2466,23 +2466,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.15 Scatter plot of percentage of Canadians reporting a language as their mother tongue vs the primary language at home colored by language category with the legend edited.#

@@ -2577,23 +2577,23 @@

4.5.3. Axis transformation and colored s
-
+

Fig. 4.16 Scatter plot of percentage of Canadians reporting a language as their mother tongue vs the primary language at home colored by language category with custom colors and shapes.#

@@ -2679,23 +2679,23 @@

4.5.3. Axis transformation and colored s
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+

Fig. 4.17 Scatter plot of percentage of Canadians reporting a language as their mother tongue vs the primary language at home colored by language category with custom colors and mouse hover tooltip.#

@@ -3125,23 +3125,23 @@

4.5.4. Bar plots: the island landmass da
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Fig. 4.18 Bar plot of Earth’s landmass sizes. The plot is too wide with the default settings.#

@@ -3226,23 +3226,23 @@

4.5.4. Bar plots: the island landmass da
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Fig. 4.19 Bar plot of size for Earth’s largest 12 landmasses.#

@@ -3337,23 +3337,23 @@

4.5.4. Bar plots: the island landmass da
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Fig. 4.20 Bar plot of size for Earth’s largest 12 landmasses, colored by landmass type, with clearer axes and labels.#

@@ -3565,23 +3565,23 @@

4.5.4. Bar plots: the island landmass da
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Fig. 4.21 A bar chart of Michelson’s speed of light data.#

@@ -3656,23 +3656,23 @@

4.5.4. Bar plots: the island landmass da
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Fig. 4.22 Histogram of Michelson’s speed of light data.#

@@ -3778,23 +3778,23 @@

Adding layers to an
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Fig. 4.23 Histogram of Michelson’s speed of light data with vertical line indicating the true speed of light.#

@@ -3872,23 +3872,23 @@

Adding layers to an
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Fig. 4.24 Histogram of Michelson’s speed of light data colored by experiment.#

@@ -3996,23 +3996,23 @@

Adding layers to an
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Fig. 4.25 Histogram of Michelson’s speed of light data colored by experiment as a categorical variable.#

@@ -4099,23 +4099,23 @@

Adding layers to an
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Fig. 4.26 Histogram of Michelson’s speed of light data split vertically by experiment.#

@@ -4331,23 +4331,23 @@

Adding layers to an
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Fig. 4.27 Histogram of relative error split vertically by experiment with clearer axes and labels#

@@ -4421,23 +4421,23 @@

Choosing a binwidth for histograms
-
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Fig. 4.28 Histogram of Michelson’s speed of light data.#

@@ -4509,23 +4509,23 @@

Choosing a binwidth for histograms
-
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Fig. 4.29 Effect of varying number of max bins on histograms.#

diff --git a/pull313/wrangling.html b/pull313/wrangling.html index 798c5aa4..7125bb8a 100644 --- a/pull313/wrangling.html +++ b/pull313/wrangling.html @@ -4803,7 +4803,7 @@

3.9. Performing operations on groups of

-
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f1025eb0e10>
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<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fd61bc32490>