读取和保存TFRecord文件 TFRecord基础API `Example`对象的创建和序列化 TFRecord文件的读写 写入TFRecord文件 读取TFRecord文件 使用tf.data读取和写入数据文件 准备加州房价数据集并将其标准化: from sklearn.datasets import fetch_california_housing from sklearn.model_selection import train_test_split from sklea...
from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() from sklearn.model_selection import train_test_split x_train_all, x_test, y_train_all, y_test = train_test_split( housing.data, housing.target, random_state = 7 ) # 切分训练集和验证集 x_train, x...
House Price Prediction Model Let’s take the following linear regression model that predicts the target value: the median house price based on the input features. The model is built using theCalifornia housing dataset: # house_price_prediction.py from sklearn.datasets import fetch_california_housing...
This example estimates the median and 95% prediction interval for the California housing dataset in a 5-fold corss validation. import numpy as np from pyquantrf import QuantileRandomForestRegressor from sklearn.datasets import fetch_california_housing qrf = QuantileRandomForestRegressor(nthreads = 4,...
housing=fetch_california_housing()print(housing.DESCR)print(housing.data.shape)print(housing.target.shape) (3)数据分割及归一化 fromsklearn.model_selectionimporttrain_test_splitfromsklearn.preprocessingimportStandardScaler x_train_all,x_test,y_train_all,y_test=train_test_split(housing.data,housing.tar...
fromsklearn.datasetsimportfetch_california_housing housing = fetch_california_housing()fromsklearn.model_selectionimporttrain_test_split x_train_all, x_test, y_train_all, y_test = train_test_split( housing.data, housing.target, random_state =7) ...
data = fetch_california_housing() X, y = data.data, data.target feature_names = data.feature_names Then, we split the data set into 80% training and 20% test: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_...
automl import AutoML # mljar-supervised # Load the data housing = fetch_california_housing() X_train, X_test, y_train, y_test = train_test_split( pd.DataFrame(housing.data, columns=housing.feature_names), housing.target, test_size=0.25, random_state=123, ) # train models with AutoML ...
requests.get("https://url_to_my_fantastic_dataset")# ORpd.read_csv("C:\\Users\\Valerio\\Downloads\\my_fantastic_dataset.csv")# ORsklearn.dataset.fetch_california_housing()# requires internet to download the data The newPython in Excelextension is fully powered byAnaconda:Anaconda...
data = fetch_california_housing(as_frame=True) housing_data = data.frame housing_data.rename(columns={'MedHouseVal': 'target'}, inplace=True) housing_data['prediction'] = housing_data['target'].values + np.random.normal(0, 5, housing_data.shape[0]) ...