因此,StandardScaler 不能保证在异常值存在的情况下平衡特征尺度。 MinMaxScaler 数据集,使所有特征值都在 [0, 1] 范围内,如下面右侧面板所示。但是,对于转换后的家庭数量,此缩放比例会压缩窄范围 [0, 0.005] 内的所有异常值。 原文由 Simas Joneliunas 发布,翻译遵循 CC BY-SA 4.0 许可协议有用 回复 查看全...
Standard Scaler Batch Predict,Platform For AI:Standard Scaler Batch Predict is a machine learning algorithm used for data preprocessing, aimed at standardizing batch data to mitigate the impact of different scales and ranges across columns. The a...
Standard Scaler Train,Platform For AI:In data preprocessing, user can standardize data to reduce the effect caused by the data volume and range in each column. After standardization, data in different columns falls within the same data ra...
synchronize #27113 AlexanderFabisch:feature/standard_scaler_array_api Status Success Total duration 18s Artifacts – wheels.yml on: pull_request Check build trigger 5s Matrix: build_wheels 1 job completed Show all jobs Source distribution 0s update-tracker / update_tracking_issue 0s U...
I believe the issue is that fit_transform does not really store the mean and standard deviation. You need to first fit the transform using: scaler = preprocessing.StandardScaler(); scaler.fit(X) df_scaled = scaler.transform(X) # In your prediction step ...
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Standard Scaler ValueError Question: I am getting a error as: ValueError: Expected 2D array, got 1D array instead: array=[ 45000. 50000. 60000. 80000. 110000. 150000. 200000. 1000000.]. When I execute the line y = sc_y.fit_transform(y)...
scaler = StandardScaler() scaler.fit(blood_df) blood_scaled = scaler.transform(blood_df) 得到blood_scaled。使用blood_scaled['dbp'].describe()我得到: count 1.000000e+01 mean 4.618528e-15 std 1.054093e+00 min -2.163355e+00 25% -4.489983e-01 ...
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