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• Kivutio cha CodeW3C.com

Feature Scaling (Scale Features)

It may be difficult to compare your data when it has different values, even using different units of measurement. How many kilograms is compared to meters? Or altitude compared to time?

The answer to this question is scaling. We can scale the data to new values that are easy to compare.

Please see the table below, which is the same as what we haveMbinu ya Kurekoda MawasilianoData set used in the chapter is the same, but this time, the unit of Volume column is liters instead of ccm (1.0 instead of 1000).

Hii inavyoweza kumwambii kusikitisha 1.0 na 790, lakini kama tukusaidia kuzingatia wanao kufikiria, tunaweza kusikitisha kwa kawaida kwamba matokeo yana kwa kawaida.

Kuna vifaa vya kuzingatia data zaidi, katika mjadala huu, tuta kutumia mchezo wa kuzingatia (standardization).

Mchezo wa kuzingatia vinatokana na muundo wa kifungu:

z = (x - u) / s

kwa sababu z ni matokeo ya kina, x ni matokeo ya kwanza, u ni kimoja, s ni kimoja cha msingi.

Ikiwa unapata data kutoka kwenye kifungu cha juu, weight kifungu, matokeo ya kwanza ni 790, matokeo ya kuzingatia ni:

(790 - 1292.23) / 238.74 = -2.1

Ikiwa unapata data kutoka kwenye kifungu cha juu, volume kifungu, matokeo ya kwanza ni 1.0, matokeo ya kuzingatia ni:

(1.0 - 1.61) / 0.38 = -1.59

Sasa, unaweza kusikitisha -2.1 na -1.59, ikisikitisha 790 na 1.0.

Hakuna hiyo hatua ambayo inahitaji kufanywa kwa sababu Python sklearn moduli ina kifungu kinachoitwa StandardScaler() mchezo huo, ambao hupya kwa kuzingatia kifungu cha data kwa kifungu cha Scaler cha kuzingatia.

Mfano

Kuwa na kuzingatia matokeo yote ya kifungu Weight na Volume:

import pandas
from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
df = pandas.read_csv("cars2.csv")
X = df[['Weight', 'Volume']]
scaledX = scale.fit_transform(X)
print(scaledX)

Matokeo:

Tangiaza, vitabu kwanza ni -2.1 na -1.59, inaendana na mafanikio yetu:

[[-2.10389253 -1.59336644]]
 [-0.55407235 -1.07190106]
 [-1.52166278 -1.59336644]
 [-1.78973979 -1.85409913]
 [-0.63784641 -0.28970299]
 [-1.52166278 -1.59336644]
 [-0.76769621 -0.55043568]
 [ 0.3046118  -0.28970299]
 [-0.7551301  -0.28970299]
 [-0.59595938 -0.0289703 ]
 [-1.30803892 -1.33263375]
 [-1.26615189 -0.81116837]
 [-0.7551301  -1.59336644]
 [-0.16871166 -0.0289703 ]
 [ 0.14125238 -0.0289703 ]
 [ 0.15800719 -0.0289703 ]
 [ 0.3046118  -0.0289703 ]
 [-0.05142797  1.53542584]
 [-0.72580918 -0.0289703 ]
 [ 0.14962979  1.01396046]
 [ 1.2219378  -0.0289703 ]
 [ 0.5685001   1.01396046]
 [ 0.3046118   1.27469315]
 [ 0.51404696 -0.0289703 ]
 [ 0.51404696  1.01396046]
 [ 0.72348212 -0.28970299]
 [ 0.8281997   1.01396046]
 [ 1.81254495  1.01396046]
 [ 0.96642691 -0.0289703 ]
 [ 1.72877089  1.01396046]
 [ 1.30990057  1.27469315]
 [ 1.90050772  1.01396046]
 [-0.23991961 -0.0289703 ]
 [ 0.40932938 -0.0289703 ]
 [ 0.47215993 -0.0289703 ]
 [ 0.4302729   2.31762392]

Kumaliza Mfano

预测 CO2 值

Mbinu ya Kurekoda Mawasiliano一章的任务是在仅知道汽车的重量和排量的情况下预测其排放的二氧化碳。

Kwa sababu ya kushakilisha matokeo, inahitajika kuitumia kinaendelea ya uharibifu wa data:

Mfano

Tathmini kiwango cha kifungu cha kutoa nguvu ya mafungu ya 2300 kilogramu na 1.3 siri:

import pandas
from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
df = pandas.read_csv("cars2.csv")
X = df[['Weight', 'Volume']]
y = df['CO2']
scaledX = scale.fit_transform(X)
regr = linear_model.LinearRegression()
regr.fit(scaledX, y)
scaled = scale.transform([[2300, 1.3]])
predictedCO2 = regr.predict([scaled[0]])
print(predictedCO2)

Matokeo:

[107.2087328]

Kumaliza Mfano

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