virginica_data = X_arr[y_arr == 2] plt.scatter(virginica_data[:, 0], virginica_data[:, 1], color="b", label="Iris_virginica") plt.legend() plt.title('Iris plants dataset,Instances=150, Attributes=4') plt.show() iris_data_plot(X_arr, y_arr) 1. 2. 3. 4. 5. 6. 7. 8...
title("Iris Dataset - Scatter Plot") plt.xlabel(iris.feature_names[0]) # 第一个特征名称 plt.ylabel(iris.feature_names[1]) # 第二个特征名称 plt.show() # 显示图像 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 这段代码生成了一个散点图,展示了不同类别的鸢尾花在前两个特征空间中的分布...
import plotly.express as px iris = px.data.iris() fig = px.scatter(iris, x="sepal_width",...
# Targets print(iris_df.target) # Target Names print(iris_df.target_names) label = {0: 'red', 1: 'blue', 2: 'green'} # Dataset Slicing x_axis = iris_df.data[:, 0] # Sepal Length y_axis = iris_df.data[:, 2] # Sepal Width # Plotting plt.scatter(x_axis, y_axis, c=i...
# Python script using Scikit-learn # for Decision Tree Classifier # Sample Decision Tree Classifier from sklearn import datasets from sklearn import metrics from sklearn.tree import DecisionTreeClassifier # load the iris datasets dataset = datasets.load_iris() ...
load_iris() ##导入iris数据集 print(iris) ##结果太长不作展示 也可以在Spyder的对象查看器中点点鼠标,进行查看。如下图。 3.2 高级画图seaborn包所带数据集 和sciki-learn包类似,seaborn高级画图包也带有一些经典的数据集,比如Titanic。 import seaborn as sns titanic=sns.load_dataset('titanic') ##加载...
defmain():# Load the datasetdata = datasets.load_iris()X = data.datay = data.target # 将数据集 X 映射到低维空间X_trans = PCA().transform(X) x1 = X_trans[:,0]x2 = X_trans[:,1] cmap = plt.get_cm...
5-2 - 从 SkLearn 导入 Iris 示例Python 复制 from sklearn import datasets import pandas as pd # SkLearn has the Iris sample dataset built in to the package iris = datasets.load_iris() df = pd.DataFrame(iris.data, columns=iris.feature_names) 5-3 - 使用 Revoscalepy API 创建表并...
fromazureml.coreimportDataset dataset = Dataset.Tabular.from_delimited_files(path = [(datastore,'train-dataset/tabular/iris.csv')]) dataset.take(3).to_pandas_dataframe() 以下示例演示如何创建引用多个文件 URL 的FileDataset。 Python fromazureml.core.datasetimportDataset url_paths = ['http://yann....
1. Boston House Prices Dataset 2. Iris Plants Dataset 3. Diabetes Dataset 4. Digits Dataset 5. Wine Recognition Dataset 6. Breast Cancer Dataset In this tutorial, we will employ the Iris Plants Dataset with the assistance of Scikit-learn. The dataset comprises parameters such as sepal length,...