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- Machine Learning Data Visualization
- Machine Learning - Data Visualization
- Machine Learning - Histograms
- Machine Learning - Density Plots
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- Statistics for Machine Learning
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- Clustering Algorithms In ML
- Machine Learning - Clustering Algorithms
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- Machine Learning - Mean-Shift Clustering
- Machine Learning - Hierarchical Clustering
- Machine Learning - Density-Based Clustering
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- Machine Learning - HDBSCAN Clustering
- Machine Learning - BIRCH Clustering
- Machine Learning - Affinity Propagation
- Machine Learning - Distribution-Based Clustering
- Machine Learning - Agglomerative Clustering
- Dimensionality Reduction In ML
- Machine Learning - Dimensionality Reduction
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- Machine Learning - Backward Elimination
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- Machine Learning - High Correlation Filter
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- Machine Learning - Missing Values Ratio
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- Machine Learning - Discussion
Machine Learning - Density Plots
A density plot is a type of plot that shows the probability density function of a continuous variable. It is similar to a histogram, but instead of using bars to represent the frequency of each value, it uses a smooth curve to represent the probability density function. The xaxis represents the range of values of the variable, and the y-axis represents the probability density.
Density plots are useful for identifying patterns in data, such as skewness, modality, and outliers. Skewness refers to the degree of asymmetry in the distribution of the variable. Modality refers to the number of peaks in the distribution. Outliers are data points that fall outside of the range of typical values for the variable.
Python Implementation of Density Plots
Python provides several libraries for data visualization, such as Matplotlib, Seaborn, Plotly, and Bokeh. For our example given below, we will use Seaborn to implement density plots.
We will use the breast cancer dataset from the Sklearn library for this example. The breast cancer dataset contains information about the characteristics of breast cancer cells and whether they are malignant or benign. The dataset has 30 features and 569 samples.
Example
Let's start by importing the necessary libraries and loading the dataset −
import matplotlib.pyplot as plt import seaborn as sns from sklearn.datasets import load_breast_cancer data = load_breast_cancer()
Next, we will create a density plot of the mean radius feature of the dataset −
plt.figure(figsize=(7.2, 3.5))
sns.kdeplot(data.data[:,0], shade=True)
plt.xlabel('Mean Radius')
plt.ylabel('Density')
plt.show()
In this code, we have used the kdeplot() function from Seaborn to create a density plot of the mean radius feature of the dataset. We have set the shade parameter to True to shade the area under the curve. We have also added labels to the x and y axes using the xlabel() and ylabel() functions.
Output
The resulting density plot shows the probability density function of mean radius values in the dataset. We can see that the data is roughly normally distributed, with a peak around 12-14.
Density Plot with Multiple Data Sets
We can also create a density plot with multiple data sets to compare their probability density functions. Let's create density plots of the mean radius feature for both the malignant and benign samples −
Example
plt.figure(figsize=(7.5, 3.5))
sns.kdeplot(data.data[data.target==0,0], shade=True, label='Malignant')
sns.kdeplot(data.data[data.target==1,0], shade=True, label='Benign')
plt.xlabel('Mean Radius')
plt.ylabel('Density')
plt.legend()
plt.show()
In this code, we have used the kdeplot() function twice to create two density plots of the mean radius feature, one for the malignant samples and one for the benign samples. We have set the shade parameter to True to shade the area under the curve, and we have added labels to the plots using the label parameter. We have also added a legend to the plot using the legend() function.
Output
On executing this code, you will get the following plot as the output −
The resulting density plot shows the probability density functions of mean radius values for both the malignant and benign samples. We can see that the probability density function for the malignant samples is shifted to the right, indicating a higher mean radius value.