Multiple regression (using machine learning - how plot data) The 2019 Stack Overflow Developer Survey Results Are InRegression Model for explained model(Details inside)Multiple linear regression, fMRIInterpreting Multiple Linear RegressionHow to plot similarity of two datasets?Best model for Machine LearningHow to plot using facet_wrap, over multiple pages as a .pdf files in r cranHow to replace NA Data using a regression?Using multiple machine learning algorithms togetherResidual plotting - why do we want to know the error?Machine learning using python
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Multiple regression (using machine learning - how plot data)
The 2019 Stack Overflow Developer Survey Results Are InRegression Model for explained model(Details inside)Multiple linear regression, fMRIInterpreting Multiple Linear RegressionHow to plot similarity of two datasets?Best model for Machine LearningHow to plot using facet_wrap, over multiple pages as a .pdf files in r cranHow to replace NA Data using a regression?Using multiple machine learning algorithms togetherResidual plotting - why do we want to know the error?Machine learning using python
$begingroup$
I wonder how I can use machine learning to plot multiple linear regression in a figure. I have one independent variable (prices of apartments) and five independent (floor, builtyear, roomnumber, square meter, kr/sqm).
The task is first to use machine learning which gives the predicted values and the actual values. Then you have to plot those values in a figure.
I have used this code:
x_train, x_test, y_train, y_test = tts(xx1, y, test_size=3)
Outcome: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
normalize=False)
regr.fit(x_train, y_train)
Outcome:nothing
regr.predict(x_test)
Outcome: array([2.37671029, 3.91651234, 2.98472475])
np.mean((regr.predict(x_test) - y_test) ** 2)
Outcome: 2.976924398032532e-26
How can I plot the actual values of the dependent variable and the predicted ones in the same figure?
machine-learning linear-regression plotting
edited Mar 29 at 1:36
Ethan
703525
asked Mar 28 at 15:04
HeddHedd
1
$endgroup$
add a comment |
$begingroup$
I wonder how I can use machine learning to plot multiple linear regression in a figure. I have one independent variable (prices of apartments) and five independent (floor, builtyear, roomnumber, square meter, kr/sqm).
The task is first to use machine learning which gives the predicted values and the actual values. Then you have to plot those values in a figure.
I have used this code:
x_train, x_test, y_train, y_test = tts(xx1, y, test_size=3)
Outcome: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
normalize=False)
regr.fit(x_train, y_train)
Outcome:nothing
regr.predict(x_test)
Outcome: array([2.37671029, 3.91651234, 2.98472475])
np.mean((regr.predict(x_test) - y_test) ** 2)
Outcome: 2.976924398032532e-26
How can I plot the actual values of the dependent variable and the predicted ones in the same figure?
machine-learning linear-regression plotting
edited Mar 29 at 1:36
Ethan
703525
asked Mar 28 at 15:04
HeddHedd
1
$endgroup$
add a comment |
$begingroup$
I wonder how I can use machine learning to plot multiple linear regression in a figure. I have one independent variable (prices of apartments) and five independent (floor, builtyear, roomnumber, square meter, kr/sqm).
The task is first to use machine learning which gives the predicted values and the actual values. Then you have to plot those values in a figure.
I have used this code:
x_train, x_test, y_train, y_test = tts(xx1, y, test_size=3)
Outcome: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
normalize=False)
regr.fit(x_train, y_train)
Outcome:nothing
regr.predict(x_test)
Outcome: array([2.37671029, 3.91651234, 2.98472475])
np.mean((regr.predict(x_test) - y_test) ** 2)
Outcome: 2.976924398032532e-26
How can I plot the actual values of the dependent variable and the predicted ones in the same figure?
machine-learning linear-regression plotting
edited Mar 29 at 1:36
Ethan
703525
asked Mar 28 at 15:04
HeddHedd
1
$endgroup$
I wonder how I can use machine learning to plot multiple linear regression in a figure. I have one independent variable (prices of apartments) and five independent (floor, builtyear, roomnumber, square meter, kr/sqm).
The task is first to use machine learning which gives the predicted values and the actual values. Then you have to plot those values in a figure.
I have used this code:
x_train, x_test, y_train, y_test = tts(xx1, y, test_size=3)
Outcome: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
normalize=False)
regr.fit(x_train, y_train)
Outcome:nothing
regr.predict(x_test)
Outcome: array([2.37671029, 3.91651234, 2.98472475])
np.mean((regr.predict(x_test) - y_test) ** 2)
Outcome: 2.976924398032532e-26
How can I plot the actual values of the dependent variable and the predicted ones in the same figure?
machine-learning linear-regression plotting
machine-learning linear-regression plotting
edited Mar 29 at 1:36
Ethan
703525
asked Mar 28 at 15:04
HeddHedd
1
edited Mar 29 at 1:36
Ethan
703525
asked Mar 28 at 15:04
HeddHedd
1
edited Mar 29 at 1:36
Ethan
703525
edited Mar 29 at 1:36
Ethan
703525
edited Mar 29 at 1:36
Ethan
703525
703525
asked Mar 28 at 15:04
HeddHedd
1
asked Mar 28 at 15:04
HeddHedd
1
asked Mar 28 at 15:04
HeddHedd
1
1
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
There might possibly be a better way but one way of doing this is to map the variable to different aesthetics of the graph. I used python and used library plotninne which is as I understand tries to mimic ggplot2 from R.
from plotnine import *
# df is the data frame that contains all the vairables as columns
(ggplot(df, aes('actual_value', 'predicted_value',
color='(dependent_var2)',
size='dependent_var3',alpha='dependent_var4',shape='factor(dependent_var5)'))
+ geom_point()
+theme(legend_title=element_text(size=8),
legend_text=element_text(size=4)
)
This will give you following graph:
answered Mar 29 at 10:55
BiranjanBiranjan
162
$endgroup$
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
There might possibly be a better way but one way of doing this is to map the variable to different aesthetics of the graph. I used python and used library plotninne which is as I understand tries to mimic ggplot2 from R.
from plotnine import *
# df is the data frame that contains all the vairables as columns
(ggplot(df, aes('actual_value', 'predicted_value',
color='(dependent_var2)',
size='dependent_var3',alpha='dependent_var4',shape='factor(dependent_var5)'))
+ geom_point()
+theme(legend_title=element_text(size=8),
legend_text=element_text(size=4)
)
This will give you following graph:
answered Mar 29 at 10:55
BiranjanBiranjan
162
$endgroup$
add a comment |
$begingroup$
There might possibly be a better way but one way of doing this is to map the variable to different aesthetics of the graph. I used python and used library plotninne which is as I understand tries to mimic ggplot2 from R.
from plotnine import *
# df is the data frame that contains all the vairables as columns
(ggplot(df, aes('actual_value', 'predicted_value',
color='(dependent_var2)',
size='dependent_var3',alpha='dependent_var4',shape='factor(dependent_var5)'))
+ geom_point()
+theme(legend_title=element_text(size=8),
legend_text=element_text(size=4)
)
This will give you following graph:
answered Mar 29 at 10:55
BiranjanBiranjan
162
$endgroup$
add a comment |
$begingroup$
There might possibly be a better way but one way of doing this is to map the variable to different aesthetics of the graph. I used python and used library plotninne which is as I understand tries to mimic ggplot2 from R.
from plotnine import *
# df is the data frame that contains all the vairables as columns
(ggplot(df, aes('actual_value', 'predicted_value',
color='(dependent_var2)',
size='dependent_var3',alpha='dependent_var4',shape='factor(dependent_var5)'))
+ geom_point()
+theme(legend_title=element_text(size=8),
legend_text=element_text(size=4)
)
This will give you following graph:
answered Mar 29 at 10:55
BiranjanBiranjan
162
$endgroup$
There might possibly be a better way but one way of doing this is to map the variable to different aesthetics of the graph. I used python and used library plotninne which is as I understand tries to mimic ggplot2 from R.
from plotnine import *
# df is the data frame that contains all the vairables as columns
(ggplot(df, aes('actual_value', 'predicted_value',
color='(dependent_var2)',
size='dependent_var3',alpha='dependent_var4',shape='factor(dependent_var5)'))
+ geom_point()
+theme(legend_title=element_text(size=8),
legend_text=element_text(size=4)
)
This will give you following graph:
answered Mar 29 at 10:55
BiranjanBiranjan
162
answered Mar 29 at 10:55
BiranjanBiranjan
162
answered Mar 29 at 10:55
BiranjanBiranjan
162
answered Mar 29 at 10:55
BiranjanBiranjan
162
162
add a comment |
add a comment |
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