Fig. 5.2 Scatter plot of concavity versus perimeter with new observation represented as a red diamond.#
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 @@ 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 @@ Fig. 5.5 Scatter plot of concavity versus perimeter with three nearest neighbors.# Fig. 5.6 Scatter plot of concavity versus perimeter with new observation represented as a red diamond.# Fig. 5.7 Scatter plot of concavity versus perimeter with 5 nearest neighbors circled.# Fig. 5.9 Comparison of K = 3 nearest neighbors with standardized and unstandardized data.# Fig. 5.10 Close-up of three nearest neighbors for unstandardized data.# Fig. 5.12 Imbalanced data with 7 nearest neighbors to a new observation highlighted.# Fig. 5.13 Imbalanced data with background color indicating the decision of the classifier and the points represent the labeled data.# Fig. 5.14 Upsampled data with background color indicating the decision of the classifier.# Fig. 6.5 Plot of estimated accuracy versus the number of neighbors.# Fig. 6.6 Plot of accuracy estimate versus number of neighbors for many K values.# Fig. 6.7 Effect of K in overfitting and underfitting.# Fig. 6.9 Effect of inclusion of irrelevant predictors.# Fig. 6.10 Tuned number of neighbors for varying number of irrelevant predictors.# Fig. 6.11 Accuracy versus number of irrelevant predictors for tuned and untuned number of neighbors.# Fig. 6.12 Estimated accuracy versus the number of predictors for the sequence of models built using forward selection.# Fig. 9.2 Scatter plot of standardized bill length versus standardized flipper length.# Fig. 9.3 Scatter plot of standardized bill length versus standardized flipper length with colored groups.# Fig. 9.4 Cluster 0 from the Fig. 9.5 Cluster 0 from the Fig. 9.6 All clusters from the Fig. 9.7 Random initialization of labels.
Each cluster is depicted as a different color and shape.# Fig. 9.8 First three iterations of K-means clustering on the Fig. 9.9 Random initialization of labels.# Fig. 9.10 First four iterations of K-means clustering on the 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.# Fig. 9.12 Total WSSD for K clusters ranging from 1 to 9.# Fig. 9.13 The data colored by the cluster assignments returned by K-means.# Fig. 9.14 A plot showing the total WSSD versus the number of clusters.# Fig. 10.2 Sampling distribution of the sample proportion for sample size 40.# Fig. 10.3 Population distribution of price per night (dollars) for all Airbnb listings in Vancouver, Canada.# Fig. 10.4 Distribution of price per night (dollars) for sample of 40 Airbnb listings.# Fig. 10.5 Sampling distribution of the sample means for sample size of 40.#5.5. Classification with
Parameter value selection
5.5. Classification with
Cross-validation6.6.1. 5.5.1. Distance between points
Cross-validation6.6.1. 5.5.1. Distance between points
5.5.2. More than two explanatory variabl
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}
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5.7.1. Centering and scaling
5.7.2. Balancing
5.7.2. Balancing
5.7.2. Balancing
5.7.2. Balancing
5.7.3. Missing data
6.5. Evaluating performance with
6.6.1. Cross-validation6.6.1.
6.6.3. Under/Overfitting
6.6.3. Under/Overfitting
6.8.1. The effect of irrelevant predicto
6.8.1. The effect of irrelevant predicto
6.8.1. The effect of irrelevant predicto
6.8.3. Forward selection in
9.4. An illustrative example
9.4. An illustrative example
9.5.1. Measuring cluster quality
penguins_standardized data set example. Observations are in blue, with the cluster center highlighted in orange.#9.5.1. Measuring cluster quality
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.#9.5.1. Measuring cluster quality
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.#9.5.2. The clustering algorithm
9.5.2. The clustering algorithm
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.#9.5.3. Random restarts
9.5.3. Random restarts
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.#9.5.4. Choosing K
9.5.4. Choosing K
9.6. K-means in Python
9.6. K-means in Python
10.4.1. Sampling distributions for propo
10.4.2. Sampling distributions for means
10.4.2. Sampling distributions for means
10.4.2. Sampling distributions for means
10.4.2. Sampling distributions for means
10.4.2. Sampling distributions for means