What is the use of coefficient in in Regression
What is the meaning of coefficient values in Machine Learning. After I print
model.print_summary()
It shows, coefficient values of for each column. But I really don't know what is the meaning of coef in this?
coef exp(coef) se(coef) z p lower 0.95 upper 0.95
EXPERIENCE IN DAYS -0.0013 0.9987 0.0001 -22.8579 0.0000 -0.0015 -0.0012 ***
GENDER 0.4598 1.5838 0.0786 5.8536 0.0000 0.3059 0.6138 ***
GRADE -0.7267 0.4835 0.0444 -16.3717 0.0000 -0.8136 -0.6397 ***
STAFFING_TYPE -0.4950 0.6096 0.0413 -11.9870 0.0000 -0.5759 -0.4140 ***
Is Large coef value represents strong feature or weaker feature ?
What is the use What is the use of coefficient in in Regression?
Note: Here model represents linear regression.
linear-algebra regression machine-learning
asked Nov 27 at 4:40
Mohamed Thasin ah
1105
add a comment |
What is the meaning of coefficient values in Machine Learning. After I print
model.print_summary()
It shows, coefficient values of for each column. But I really don't know what is the meaning of coef in this?
coef exp(coef) se(coef) z p lower 0.95 upper 0.95
EXPERIENCE IN DAYS -0.0013 0.9987 0.0001 -22.8579 0.0000 -0.0015 -0.0012 ***
GENDER 0.4598 1.5838 0.0786 5.8536 0.0000 0.3059 0.6138 ***
GRADE -0.7267 0.4835 0.0444 -16.3717 0.0000 -0.8136 -0.6397 ***
STAFFING_TYPE -0.4950 0.6096 0.0413 -11.9870 0.0000 -0.5759 -0.4140 ***
Is Large coef value represents strong feature or weaker feature ?
What is the use What is the use of coefficient in in Regression?
Note: Here model represents linear regression.
linear-algebra regression machine-learning
asked Nov 27 at 4:40
Mohamed Thasin ah
1105
It really depends on what "model" is. If this is a linear regression model, then [roughly] the coefficient of a feature represents how much the response variable changes if you increase that feature by one unit and hold all other features constant.
– angryavian
Nov 27 at 5:07
@angryavian - Thanks for the comment, I forgot to mention that, It's a linear regression model.
– Mohamed Thasin ah
Nov 27 at 5:23
add a comment |
What is the meaning of coefficient values in Machine Learning. After I print
model.print_summary()
It shows, coefficient values of for each column. But I really don't know what is the meaning of coef in this?
coef exp(coef) se(coef) z p lower 0.95 upper 0.95
EXPERIENCE IN DAYS -0.0013 0.9987 0.0001 -22.8579 0.0000 -0.0015 -0.0012 ***
GENDER 0.4598 1.5838 0.0786 5.8536 0.0000 0.3059 0.6138 ***
GRADE -0.7267 0.4835 0.0444 -16.3717 0.0000 -0.8136 -0.6397 ***
STAFFING_TYPE -0.4950 0.6096 0.0413 -11.9870 0.0000 -0.5759 -0.4140 ***
Is Large coef value represents strong feature or weaker feature ?
What is the use What is the use of coefficient in in Regression?
Note: Here model represents linear regression.
linear-algebra regression machine-learning
asked Nov 27 at 4:40
Mohamed Thasin ah
1105
What is the meaning of coefficient values in Machine Learning. After I print
model.print_summary()
It shows, coefficient values of for each column. But I really don't know what is the meaning of coef in this?
coef exp(coef) se(coef) z p lower 0.95 upper 0.95
EXPERIENCE IN DAYS -0.0013 0.9987 0.0001 -22.8579 0.0000 -0.0015 -0.0012 ***
GENDER 0.4598 1.5838 0.0786 5.8536 0.0000 0.3059 0.6138 ***
GRADE -0.7267 0.4835 0.0444 -16.3717 0.0000 -0.8136 -0.6397 ***
STAFFING_TYPE -0.4950 0.6096 0.0413 -11.9870 0.0000 -0.5759 -0.4140 ***
Is Large coef value represents strong feature or weaker feature ?
What is the use What is the use of coefficient in in Regression?
Note: Here model represents linear regression.
linear-algebra regression machine-learning
linear-algebra regression machine-learning
asked Nov 27 at 4:40
Mohamed Thasin ah
1105
asked Nov 27 at 4:40
Mohamed Thasin ah
1105
edited Nov 27 at 5:24
asked Nov 27 at 4:40
Mohamed Thasin ah
1105
asked Nov 27 at 4:40
Mohamed Thasin ah
1105
asked Nov 27 at 4:40
Mohamed Thasin ah
1105
1105
It really depends on what "model" is. If this is a linear regression model, then [roughly] the coefficient of a feature represents how much the response variable changes if you increase that feature by one unit and hold all other features constant.
– angryavian
Nov 27 at 5:07
@angryavian - Thanks for the comment, I forgot to mention that, It's a linear regression model.
– Mohamed Thasin ah
Nov 27 at 5:23
add a comment |
It really depends on what "model" is. If this is a linear regression model, then [roughly] the coefficient of a feature represents how much the response variable changes if you increase that feature by one unit and hold all other features constant.
– angryavian
Nov 27 at 5:07
@angryavian - Thanks for the comment, I forgot to mention that, It's a linear regression model.
– Mohamed Thasin ah
Nov 27 at 5:23
It really depends on what "model" is. If this is a linear regression model, then [roughly] the coefficient of a feature represents how much the response variable changes if you increase that feature by one unit and hold all other features constant.
– angryavian
Nov 27 at 5:07
It really depends on what "model" is. If this is a linear regression model, then [roughly] the coefficient of a feature represents how much the response variable changes if you increase that feature by one unit and hold all other features constant.
– angryavian
Nov 27 at 5:07
@angryavian - Thanks for the comment, I forgot to mention that, It's a linear regression model.
– Mohamed Thasin ah
Nov 27 at 5:23
@angryavian - Thanks for the comment, I forgot to mention that, It's a linear regression model.
– Mohamed Thasin ah
Nov 27 at 5:23
add a comment |
1 Answer
1
active
oldest
votes
When you have a linear regression model with $n$ exogenic variables, the model is
$$
y = beta_0 + beta_1 x_1 + beta_2 x_2 + ldots + beta_n x_n + xi
$$
So you are looking for coefficients $beta_1$, $beta_2$, $ldots$, $beta_n$, so that the difference between the output ($y$) of your model for a given input vector $(x_1, dots, x_n)$ compared to your data is minimized.
In other words the coefficients determine how much a change of each input variable contributes to the output variable. For example, a coefficient of 0.4598 for your variable $x_2$ (Gender) means that the output variable $y$ increases for 0.4598 if the variable Gender increases for 1.
answered Nov 27 at 5:36
WolfgangP
1625
Thanks for the answer, Can I consider GENDER is a strong feature among them?
– Mohamed Thasin ah
Nov 27 at 5:41
Strong is not the correct word here. Read the coefficients like this: if GENDER increases for 1, the output variable increases for 0.4598. If EXPERIENCE IN DAYS increases for 1, the output variable decreases for -0.0013 (given that all other input variables stay the same), etc.
– WolfgangP
Nov 27 at 5:50
So Coeff represents influence of outcome ofyright?
– Mohamed Thasin ah
Nov 27 at 5:56
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
When you have a linear regression model with $n$ exogenic variables, the model is
$$
y = beta_0 + beta_1 x_1 + beta_2 x_2 + ldots + beta_n x_n + xi
$$
So you are looking for coefficients $beta_1$, $beta_2$, $ldots$, $beta_n$, so that the difference between the output ($y$) of your model for a given input vector $(x_1, dots, x_n)$ compared to your data is minimized.
In other words the coefficients determine how much a change of each input variable contributes to the output variable. For example, a coefficient of 0.4598 for your variable $x_2$ (Gender) means that the output variable $y$ increases for 0.4598 if the variable Gender increases for 1.
answered Nov 27 at 5:36
WolfgangP
1625
Thanks for the answer, Can I consider GENDER is a strong feature among them?
– Mohamed Thasin ah
Nov 27 at 5:41
Strong is not the correct word here. Read the coefficients like this: if GENDER increases for 1, the output variable increases for 0.4598. If EXPERIENCE IN DAYS increases for 1, the output variable decreases for -0.0013 (given that all other input variables stay the same), etc.
– WolfgangP
Nov 27 at 5:50
So Coeff represents influence of outcome ofyright?
– Mohamed Thasin ah
Nov 27 at 5:56
add a comment |
When you have a linear regression model with $n$ exogenic variables, the model is
$$
y = beta_0 + beta_1 x_1 + beta_2 x_2 + ldots + beta_n x_n + xi
$$
So you are looking for coefficients $beta_1$, $beta_2$, $ldots$, $beta_n$, so that the difference between the output ($y$) of your model for a given input vector $(x_1, dots, x_n)$ compared to your data is minimized.
In other words the coefficients determine how much a change of each input variable contributes to the output variable. For example, a coefficient of 0.4598 for your variable $x_2$ (Gender) means that the output variable $y$ increases for 0.4598 if the variable Gender increases for 1.
answered Nov 27 at 5:36
WolfgangP
1625
Thanks for the answer, Can I consider GENDER is a strong feature among them?
– Mohamed Thasin ah
Nov 27 at 5:41
Strong is not the correct word here. Read the coefficients like this: if GENDER increases for 1, the output variable increases for 0.4598. If EXPERIENCE IN DAYS increases for 1, the output variable decreases for -0.0013 (given that all other input variables stay the same), etc.
– WolfgangP
Nov 27 at 5:50
So Coeff represents influence of outcome ofyright?
– Mohamed Thasin ah
Nov 27 at 5:56
add a comment |
When you have a linear regression model with $n$ exogenic variables, the model is
$$
y = beta_0 + beta_1 x_1 + beta_2 x_2 + ldots + beta_n x_n + xi
$$
So you are looking for coefficients $beta_1$, $beta_2$, $ldots$, $beta_n$, so that the difference between the output ($y$) of your model for a given input vector $(x_1, dots, x_n)$ compared to your data is minimized.
In other words the coefficients determine how much a change of each input variable contributes to the output variable. For example, a coefficient of 0.4598 for your variable $x_2$ (Gender) means that the output variable $y$ increases for 0.4598 if the variable Gender increases for 1.
answered Nov 27 at 5:36
WolfgangP
1625
When you have a linear regression model with $n$ exogenic variables, the model is
$$
y = beta_0 + beta_1 x_1 + beta_2 x_2 + ldots + beta_n x_n + xi
$$
So you are looking for coefficients $beta_1$, $beta_2$, $ldots$, $beta_n$, so that the difference between the output ($y$) of your model for a given input vector $(x_1, dots, x_n)$ compared to your data is minimized.
In other words the coefficients determine how much a change of each input variable contributes to the output variable. For example, a coefficient of 0.4598 for your variable $x_2$ (Gender) means that the output variable $y$ increases for 0.4598 if the variable Gender increases for 1.
answered Nov 27 at 5:36
WolfgangP
1625
edited Nov 27 at 5:54
answered Nov 27 at 5:36
WolfgangP
1625
answered Nov 27 at 5:36
WolfgangP
1625
answered Nov 27 at 5:36
WolfgangP
1625
1625
Thanks for the answer, Can I consider GENDER is a strong feature among them?
– Mohamed Thasin ah
Nov 27 at 5:41
Strong is not the correct word here. Read the coefficients like this: if GENDER increases for 1, the output variable increases for 0.4598. If EXPERIENCE IN DAYS increases for 1, the output variable decreases for -0.0013 (given that all other input variables stay the same), etc.
– WolfgangP
Nov 27 at 5:50
So Coeff represents influence of outcome ofyright?
– Mohamed Thasin ah
Nov 27 at 5:56
add a comment |
Thanks for the answer, Can I consider GENDER is a strong feature among them?
– Mohamed Thasin ah
Nov 27 at 5:41
Strong is not the correct word here. Read the coefficients like this: if GENDER increases for 1, the output variable increases for 0.4598. If EXPERIENCE IN DAYS increases for 1, the output variable decreases for -0.0013 (given that all other input variables stay the same), etc.
– WolfgangP
Nov 27 at 5:50
So Coeff represents influence of outcome ofyright?
– Mohamed Thasin ah
Nov 27 at 5:56
Thanks for the answer, Can I consider GENDER is a strong feature among them?
– Mohamed Thasin ah
Nov 27 at 5:41
Thanks for the answer, Can I consider GENDER is a strong feature among them?
– Mohamed Thasin ah
Nov 27 at 5:41
Strong is not the correct word here. Read the coefficients like this: if GENDER increases for 1, the output variable increases for 0.4598. If EXPERIENCE IN DAYS increases for 1, the output variable decreases for -0.0013 (given that all other input variables stay the same), etc.
– WolfgangP
Nov 27 at 5:50
Strong is not the correct word here. Read the coefficients like this: if GENDER increases for 1, the output variable increases for 0.4598. If EXPERIENCE IN DAYS increases for 1, the output variable decreases for -0.0013 (given that all other input variables stay the same), etc.
– WolfgangP
Nov 27 at 5:50
So Coeff represents influence of outcome of
y right?– Mohamed Thasin ah
Nov 27 at 5:56
So Coeff represents influence of outcome of
y right?– Mohamed Thasin ah
Nov 27 at 5:56
add a comment |
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It really depends on what "model" is. If this is a linear regression model, then [roughly] the coefficient of a feature represents how much the response variable changes if you increase that feature by one unit and hold all other features constant.
– angryavian
Nov 27 at 5:07
@angryavian - Thanks for the comment, I forgot to mention that, It's a linear regression model.
– Mohamed Thasin ah
Nov 27 at 5:23