Mean imputation replaces missing values with the mean value of that feature/variable. Mean imputation is one of the most ‘naive’ imputation methods because unlike more complex methods like k-nearest neighbors imputation, it does not use the information we have about an observation to estimate a value for it.
import pandas as pd import numpy as np from sklearn.preprocessing import Imputer
df = pd.DataFrame() df['x0'] = [0.3051,0.4949,0.6974,0.3769,0.2231,0.341,0.4436,0.5897,0.6308,0.5] df['x1'] = [np.nan,0.2654,0.2615,0.5846,0.4615,0.8308,0.4962,0.3269,0.5346,0.6731] df
mean_imputer = Imputer(missing_values='NaN', strategy='mean', axis=0) mean_imputer = mean_imputer.fit(df)
imputed_df = mean_imputer.transform(df.values)
array([[ 0.3051 , 0.49273333], [ 0.4949 , 0.2654 ], [ 0.6974 , 0.2615 ], [ 0.3769 , 0.5846 ], [ 0.2231 , 0.4615 ], [ 0.341 , 0.8308 ], [ 0.4436 , 0.4962 ], [ 0.5897 , 0.3269 ], [ 0.6308 , 0.5346 ], [ 0.5 , 0.6731 ]])
0.49273333 is the imputed value, replacing the
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Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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