Single machine learning algorithm for multiple classes of data : One hot encoder
0
$begingroup$
I have data of the following kind:
x1 x2 y
0 0 1 1
1 0 2 2
2 0 3 3
3 0 4 4
4 1 1 4
5 1 2 8
6 1 3 12
7 1 4 16
Is it possible to construct a single machine learning algorithm in python/scikit-learn by defining column x1 in such a way that a simple linear regression should give predict(x1=0, x2=5) = 5 and predict(x1=1, x2=5) = 20. My actual problem has multiple values of x1.
To illustrate the problem better: I have the following code with one hot encoder and it doesn't seem to give the accuracy of training the data separately.
import pandas as pd
from sklearn.linear_model import LinearRegression
# Dataframe with x1 = 0 and linear regression gives a slope of 1 as expected
df = pd.DataFrame(data=[{'x1': 0, 'x2': 1, 'y': 1},
{'x1': 0, 'x2': 2, 'y': 2},
{'x1': 0, 'x2': 3, 'y': 3},
{'x1': 0, 'x2': 4, 'y': 4}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[0, 5]]))) # Output is 5 as expected
# Dataframe with x1 = 1 and linear regression gives a slope of 5 as expected
df = pd.DataFrame(data=[{'x1': 1, 'x2': 1, 'y': 4},
{'x1': 1, 'x2': 2, 'y': 8},
{'x1': 1, 'x2': 3, 'y': 12},
{'x1': 1, 'x2': 4, 'y': 16}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[1, 5]]))) # Output is 20 as expected
# Combine the two data frames x1 = 0 and x1 = 1
df = pd.DataFrame(data=[{'x1': 0, 'x2': 1, 'y': 1},
{'x1': 0, 'x2': 2, 'y': 2},
{'x1': 0, 'x2': 3, 'y': 3},
{'x1': 0, 'x2': 4, 'y': 4},
{'x1': 1, 'x2': 1, 'y': 4},
{'x1': 1, 'x2': 2, 'y': 8},
{'x1': 1, 'x2': 3, 'y': 12},
{'x1': 1, 'x2': 4, 'y': 16}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[0, 5]]))) # Output is 8.75
print(reg.predict(np.array([[1, 5]]))) # Output is 16.25
# use one hot encoder
df = pd.get_dummies(df, columns=["x1"], prefix=["x1"])
X = df[['x1_0', 'x1_1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[1, 0, 5]]))) # Output is 8.75
print(reg.predict(np.array([[0, 1, 5]]))) # Output is 16.25
How can I use pandas and sklearn for the combined data to get the same accuracy using one machine learning model?
machine-learning python scikit-learn
asked Nov 23 '18 at 14:31
user3631804user3631804
11
$endgroup$
bumped to the homepage by Community♦ 10 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
|
show 3 more comments
0
$begingroup$
I have data of the following kind:
x1 x2 y
0 0 1 1
1 0 2 2
2 0 3 3
3 0 4 4
4 1 1 4
5 1 2 8
6 1 3 12
7 1 4 16
Is it possible to construct a single machine learning algorithm in python/scikit-learn by defining column x1 in such a way that a simple linear regression should give predict(x1=0, x2=5) = 5 and predict(x1=1, x2=5) = 20. My actual problem has multiple values of x1.
To illustrate the problem better: I have the following code with one hot encoder and it doesn't seem to give the accuracy of training the data separately.
import pandas as pd
from sklearn.linear_model import LinearRegression
# Dataframe with x1 = 0 and linear regression gives a slope of 1 as expected
df = pd.DataFrame(data=[{'x1': 0, 'x2': 1, 'y': 1},
{'x1': 0, 'x2': 2, 'y': 2},
{'x1': 0, 'x2': 3, 'y': 3},
{'x1': 0, 'x2': 4, 'y': 4}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[0, 5]]))) # Output is 5 as expected
# Dataframe with x1 = 1 and linear regression gives a slope of 5 as expected
df = pd.DataFrame(data=[{'x1': 1, 'x2': 1, 'y': 4},
{'x1': 1, 'x2': 2, 'y': 8},
{'x1': 1, 'x2': 3, 'y': 12},
{'x1': 1, 'x2': 4, 'y': 16}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[1, 5]]))) # Output is 20 as expected
# Combine the two data frames x1 = 0 and x1 = 1
df = pd.DataFrame(data=[{'x1': 0, 'x2': 1, 'y': 1},
{'x1': 0, 'x2': 2, 'y': 2},
{'x1': 0, 'x2': 3, 'y': 3},
{'x1': 0, 'x2': 4, 'y': 4},
{'x1': 1, 'x2': 1, 'y': 4},
{'x1': 1, 'x2': 2, 'y': 8},
{'x1': 1, 'x2': 3, 'y': 12},
{'x1': 1, 'x2': 4, 'y': 16}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[0, 5]]))) # Output is 8.75
print(reg.predict(np.array([[1, 5]]))) # Output is 16.25
# use one hot encoder
df = pd.get_dummies(df, columns=["x1"], prefix=["x1"])
X = df[['x1_0', 'x1_1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[1, 0, 5]]))) # Output is 8.75
print(reg.predict(np.array([[0, 1, 5]]))) # Output is 16.25
How can I use pandas and sklearn for the combined data to get the same accuracy using one machine learning model?
machine-learning python scikit-learn
asked Nov 23 '18 at 14:31
user3631804user3631804
11
$endgroup$
bumped to the homepage by Community♦ 10 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
Welcome todatascience. This is one good link that may help you: scikit-learn.org/stable/tutorial/basic/tutorial.html
$endgroup$
– rnso
Nov 23 '18 at 15:04
$begingroup$
@rnso Thank you for the link. My issue is not about setting up a simple regression problem using scikit-learn. It is more to do with how to handle a variable like (x1) which qualitatively changes the trend of the data. In the example I gave, the ML algorithm must giveslope = 1whenx1=0andslope=4whenx1=1. Is that possible to do with a single ML algorithm or breaking up the data into two training sets is the only alternative?
$endgroup$
– user3631804
Nov 23 '18 at 15:39
$begingroup$
Probably you need mixed models as on: statsmodels.org/devel/mixed_linear.html
$endgroup$
– rnso
Nov 23 '18 at 16:15
$begingroup$
You should post some follow-up here. How did you solve your problem?
$endgroup$
– rnso
Nov 24 '18 at 8:07
$begingroup$
If x1 will have only 2 options then you can keep only one column (x1) for joint dataframe. The try to predict for (0,5) and (1,5). Post here the results.
$endgroup$
– rnso
Nov 24 '18 at 10:45
|
show 3 more comments
0
0
0
$begingroup$
I have data of the following kind:
x1 x2 y
0 0 1 1
1 0 2 2
2 0 3 3
3 0 4 4
4 1 1 4
5 1 2 8
6 1 3 12
7 1 4 16
Is it possible to construct a single machine learning algorithm in python/scikit-learn by defining column x1 in such a way that a simple linear regression should give predict(x1=0, x2=5) = 5 and predict(x1=1, x2=5) = 20. My actual problem has multiple values of x1.
To illustrate the problem better: I have the following code with one hot encoder and it doesn't seem to give the accuracy of training the data separately.
import pandas as pd
from sklearn.linear_model import LinearRegression
# Dataframe with x1 = 0 and linear regression gives a slope of 1 as expected
df = pd.DataFrame(data=[{'x1': 0, 'x2': 1, 'y': 1},
{'x1': 0, 'x2': 2, 'y': 2},
{'x1': 0, 'x2': 3, 'y': 3},
{'x1': 0, 'x2': 4, 'y': 4}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[0, 5]]))) # Output is 5 as expected
# Dataframe with x1 = 1 and linear regression gives a slope of 5 as expected
df = pd.DataFrame(data=[{'x1': 1, 'x2': 1, 'y': 4},
{'x1': 1, 'x2': 2, 'y': 8},
{'x1': 1, 'x2': 3, 'y': 12},
{'x1': 1, 'x2': 4, 'y': 16}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[1, 5]]))) # Output is 20 as expected
# Combine the two data frames x1 = 0 and x1 = 1
df = pd.DataFrame(data=[{'x1': 0, 'x2': 1, 'y': 1},
{'x1': 0, 'x2': 2, 'y': 2},
{'x1': 0, 'x2': 3, 'y': 3},
{'x1': 0, 'x2': 4, 'y': 4},
{'x1': 1, 'x2': 1, 'y': 4},
{'x1': 1, 'x2': 2, 'y': 8},
{'x1': 1, 'x2': 3, 'y': 12},
{'x1': 1, 'x2': 4, 'y': 16}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[0, 5]]))) # Output is 8.75
print(reg.predict(np.array([[1, 5]]))) # Output is 16.25
# use one hot encoder
df = pd.get_dummies(df, columns=["x1"], prefix=["x1"])
X = df[['x1_0', 'x1_1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[1, 0, 5]]))) # Output is 8.75
print(reg.predict(np.array([[0, 1, 5]]))) # Output is 16.25
How can I use pandas and sklearn for the combined data to get the same accuracy using one machine learning model?
machine-learning python scikit-learn
asked Nov 23 '18 at 14:31
user3631804user3631804
11
$endgroup$
I have data of the following kind:
x1 x2 y
0 0 1 1
1 0 2 2
2 0 3 3
3 0 4 4
4 1 1 4
5 1 2 8
6 1 3 12
7 1 4 16
Is it possible to construct a single machine learning algorithm in python/scikit-learn by defining column x1 in such a way that a simple linear regression should give predict(x1=0, x2=5) = 5 and predict(x1=1, x2=5) = 20. My actual problem has multiple values of x1.
To illustrate the problem better: I have the following code with one hot encoder and it doesn't seem to give the accuracy of training the data separately.
import pandas as pd
from sklearn.linear_model import LinearRegression
# Dataframe with x1 = 0 and linear regression gives a slope of 1 as expected
df = pd.DataFrame(data=[{'x1': 0, 'x2': 1, 'y': 1},
{'x1': 0, 'x2': 2, 'y': 2},
{'x1': 0, 'x2': 3, 'y': 3},
{'x1': 0, 'x2': 4, 'y': 4}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[0, 5]]))) # Output is 5 as expected
# Dataframe with x1 = 1 and linear regression gives a slope of 5 as expected
df = pd.DataFrame(data=[{'x1': 1, 'x2': 1, 'y': 4},
{'x1': 1, 'x2': 2, 'y': 8},
{'x1': 1, 'x2': 3, 'y': 12},
{'x1': 1, 'x2': 4, 'y': 16}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[1, 5]]))) # Output is 20 as expected
# Combine the two data frames x1 = 0 and x1 = 1
df = pd.DataFrame(data=[{'x1': 0, 'x2': 1, 'y': 1},
{'x1': 0, 'x2': 2, 'y': 2},
{'x1': 0, 'x2': 3, 'y': 3},
{'x1': 0, 'x2': 4, 'y': 4},
{'x1': 1, 'x2': 1, 'y': 4},
{'x1': 1, 'x2': 2, 'y': 8},
{'x1': 1, 'x2': 3, 'y': 12},
{'x1': 1, 'x2': 4, 'y': 16}
],
columns=['x1', 'x2', 'y'])
X = df[['x1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[0, 5]]))) # Output is 8.75
print(reg.predict(np.array([[1, 5]]))) # Output is 16.25
# use one hot encoder
df = pd.get_dummies(df, columns=["x1"], prefix=["x1"])
X = df[['x1_0', 'x1_1', 'x2']]
y = df['y']
reg = LinearRegression().fit(X, y)
print(reg.predict(np.array([[1, 0, 5]]))) # Output is 8.75
print(reg.predict(np.array([[0, 1, 5]]))) # Output is 16.25
How can I use pandas and sklearn for the combined data to get the same accuracy using one machine learning model?
machine-learning python scikit-learn
machine-learning python scikit-learn
asked Nov 23 '18 at 14:31
user3631804user3631804
11
asked Nov 23 '18 at 14:31
user3631804user3631804
11
edited Nov 24 '18 at 11:37
user3631804
asked Nov 23 '18 at 14:31
user3631804user3631804
11
asked Nov 23 '18 at 14:31
user3631804user3631804
11
asked Nov 23 '18 at 14:31
user3631804user3631804
11
11
bumped to the homepage by Community♦ 10 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 10 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
Welcome todatascience. This is one good link that may help you: scikit-learn.org/stable/tutorial/basic/tutorial.html
$endgroup$
– rnso
Nov 23 '18 at 15:04
$begingroup$
@rnso Thank you for the link. My issue is not about setting up a simple regression problem using scikit-learn. It is more to do with how to handle a variable like (x1) which qualitatively changes the trend of the data. In the example I gave, the ML algorithm must giveslope = 1whenx1=0andslope=4whenx1=1. Is that possible to do with a single ML algorithm or breaking up the data into two training sets is the only alternative?
$endgroup$
– user3631804
Nov 23 '18 at 15:39
$begingroup$
Probably you need mixed models as on: statsmodels.org/devel/mixed_linear.html
$endgroup$
– rnso
Nov 23 '18 at 16:15
$begingroup$
You should post some follow-up here. How did you solve your problem?
$endgroup$
– rnso
Nov 24 '18 at 8:07
$begingroup$
If x1 will have only 2 options then you can keep only one column (x1) for joint dataframe. The try to predict for (0,5) and (1,5). Post here the results.
$endgroup$
– rnso
Nov 24 '18 at 10:45
|
show 3 more comments
$begingroup$
Welcome todatascience. This is one good link that may help you: scikit-learn.org/stable/tutorial/basic/tutorial.html
$endgroup$
– rnso
Nov 23 '18 at 15:04
$begingroup$
@rnso Thank you for the link. My issue is not about setting up a simple regression problem using scikit-learn. It is more to do with how to handle a variable like (x1) which qualitatively changes the trend of the data. In the example I gave, the ML algorithm must giveslope = 1whenx1=0andslope=4whenx1=1. Is that possible to do with a single ML algorithm or breaking up the data into two training sets is the only alternative?
$endgroup$
– user3631804
Nov 23 '18 at 15:39
$begingroup$
Probably you need mixed models as on: statsmodels.org/devel/mixed_linear.html
$endgroup$
– rnso
Nov 23 '18 at 16:15
$begingroup$
You should post some follow-up here. How did you solve your problem?
$endgroup$
– rnso
Nov 24 '18 at 8:07
$begingroup$
If x1 will have only 2 options then you can keep only one column (x1) for joint dataframe. The try to predict for (0,5) and (1,5). Post here the results.
$endgroup$
– rnso
Nov 24 '18 at 10:45
$begingroup$
Welcome to
datascience. This is one good link that may help you: scikit-learn.org/stable/tutorial/basic/tutorial.html$endgroup$
– rnso
Nov 23 '18 at 15:04
$begingroup$
Welcome to
datascience. This is one good link that may help you: scikit-learn.org/stable/tutorial/basic/tutorial.html$endgroup$
– rnso
Nov 23 '18 at 15:04
$begingroup$
@rnso Thank you for the link. My issue is not about setting up a simple regression problem using scikit-learn. It is more to do with how to handle a variable like (
x1) which qualitatively changes the trend of the data. In the example I gave, the ML algorithm must give slope = 1 when x1=0 and slope=4 when x1=1. Is that possible to do with a single ML algorithm or breaking up the data into two training sets is the only alternative?$endgroup$
– user3631804
Nov 23 '18 at 15:39
$begingroup$
@rnso Thank you for the link. My issue is not about setting up a simple regression problem using scikit-learn. It is more to do with how to handle a variable like (
x1) which qualitatively changes the trend of the data. In the example I gave, the ML algorithm must give slope = 1 when x1=0 and slope=4 when x1=1. Is that possible to do with a single ML algorithm or breaking up the data into two training sets is the only alternative?$endgroup$
– user3631804
Nov 23 '18 at 15:39
$begingroup$
Probably you need mixed models as on: statsmodels.org/devel/mixed_linear.html
$endgroup$
– rnso
Nov 23 '18 at 16:15
$begingroup$
Probably you need mixed models as on: statsmodels.org/devel/mixed_linear.html
$endgroup$
– rnso
Nov 23 '18 at 16:15
$begingroup$
You should post some follow-up here. How did you solve your problem?
$endgroup$
– rnso
Nov 24 '18 at 8:07
$begingroup$
You should post some follow-up here. How did you solve your problem?
$endgroup$
– rnso
Nov 24 '18 at 8:07
$begingroup$
If x1 will have only 2 options then you can keep only one column (x1) for joint dataframe. The try to predict for (0,5) and (1,5). Post here the results.
$endgroup$
– rnso
Nov 24 '18 at 10:45
$begingroup$
If x1 will have only 2 options then you can keep only one column (x1) for joint dataframe. The try to predict for (0,5) and (1,5). Post here the results.
$endgroup$
– rnso
Nov 24 '18 at 10:45
|
show 3 more comments
1 Answer
1
active
oldest
votes
0
$begingroup$
You can have x1 as a categorical variable, convert it to dummy variables (one hot encoder) and then run linear regression (or any other algorithm).
answered Nov 23 '18 at 16:30
rnsornso
508317
$endgroup$
$begingroup$
Thank you. I used one hot encoder and that doesn't seem to give me the answer. I improved the question by providing pseudo-code. Can you please let me know if I did something wrong with the encoder?
$endgroup$
– user3631804
Nov 24 '18 at 10:20
add a comment |
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1 Answer
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active
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1 Answer
1
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oldest
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0
$begingroup$
You can have x1 as a categorical variable, convert it to dummy variables (one hot encoder) and then run linear regression (or any other algorithm).
answered Nov 23 '18 at 16:30
rnsornso
508317
$endgroup$
$begingroup$
Thank you. I used one hot encoder and that doesn't seem to give me the answer. I improved the question by providing pseudo-code. Can you please let me know if I did something wrong with the encoder?
$endgroup$
– user3631804
Nov 24 '18 at 10:20
add a comment |
0
$begingroup$
You can have x1 as a categorical variable, convert it to dummy variables (one hot encoder) and then run linear regression (or any other algorithm).
answered Nov 23 '18 at 16:30
rnsornso
508317
$endgroup$
$begingroup$
Thank you. I used one hot encoder and that doesn't seem to give me the answer. I improved the question by providing pseudo-code. Can you please let me know if I did something wrong with the encoder?
$endgroup$
– user3631804
Nov 24 '18 at 10:20
add a comment |
0
0
0
$begingroup$
You can have x1 as a categorical variable, convert it to dummy variables (one hot encoder) and then run linear regression (or any other algorithm).
answered Nov 23 '18 at 16:30
rnsornso
508317
$endgroup$
You can have x1 as a categorical variable, convert it to dummy variables (one hot encoder) and then run linear regression (or any other algorithm).
answered Nov 23 '18 at 16:30
rnsornso
508317
answered Nov 23 '18 at 16:30
rnsornso
508317
answered Nov 23 '18 at 16:30
rnsornso
508317
answered Nov 23 '18 at 16:30
rnsornso
508317
508317
$begingroup$
Thank you. I used one hot encoder and that doesn't seem to give me the answer. I improved the question by providing pseudo-code. Can you please let me know if I did something wrong with the encoder?
$endgroup$
– user3631804
Nov 24 '18 at 10:20
add a comment |
$begingroup$
Thank you. I used one hot encoder and that doesn't seem to give me the answer. I improved the question by providing pseudo-code. Can you please let me know if I did something wrong with the encoder?
$endgroup$
– user3631804
Nov 24 '18 at 10:20
$begingroup$
Thank you. I used one hot encoder and that doesn't seem to give me the answer. I improved the question by providing pseudo-code. Can you please let me know if I did something wrong with the encoder?
$endgroup$
– user3631804
Nov 24 '18 at 10:20
$begingroup$
Thank you. I used one hot encoder and that doesn't seem to give me the answer. I improved the question by providing pseudo-code. Can you please let me know if I did something wrong with the encoder?
$endgroup$
– user3631804
Nov 24 '18 at 10:20
add a comment |
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$begingroup$
Welcome to
datascience. This is one good link that may help you: scikit-learn.org/stable/tutorial/basic/tutorial.html$endgroup$
– rnso
Nov 23 '18 at 15:04
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@rnso Thank you for the link. My issue is not about setting up a simple regression problem using scikit-learn. It is more to do with how to handle a variable like (
x1) which qualitatively changes the trend of the data. In the example I gave, the ML algorithm must giveslope = 1whenx1=0andslope=4whenx1=1. Is that possible to do with a single ML algorithm or breaking up the data into two training sets is the only alternative?$endgroup$
– user3631804
Nov 23 '18 at 15:39
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Probably you need mixed models as on: statsmodels.org/devel/mixed_linear.html
$endgroup$
– rnso
Nov 23 '18 at 16:15
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You should post some follow-up here. How did you solve your problem?
$endgroup$
– rnso
Nov 24 '18 at 8:07
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If x1 will have only 2 options then you can keep only one column (x1) for joint dataframe. The try to predict for (0,5) and (1,5). Post here the results.
$endgroup$
– rnso
Nov 24 '18 at 10:45