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Is numpy.corrcoef() enough to find correlation?
How to get correlation between two categorical variable and a categorical variable and continuous variable?Find correlation in observed dataUse of Correlation scoreCorrelation and feature selectionExporting Correlation Matrix (from function)How decision trees work in PythonHow to interpret partial dependence interaction plot for binary classification?How to select variables based on the mean correlation in a correlation matrix?How to approach a machine learning problem?Why is my Seaborn distplot creating bouncing lines instead of smooth lines?What measures can I use to find correlation between categorical features and binary label?
$begingroup$
I am currently working through Kaggle's titanic competition and I'm trying to figure out the correlation between the Survived column and other columns. I am using numpy.corrcoef() to matrix the correlation between the columns and here is what I have:
The correlation between pClass & Survived is: [[ 1. -0.33848104]
[-0.33848104 1. ]]
The correlation between Sex & Survived is: [[ 1. -0.54335138]
[-0.54335138 1. ]]
The correlation between Age & Survived is:[[ 1. -0.07065723]
[-0.07065723 1. ]]
The correlation between Fare & Survived is: [[1. 0.25730652]
[0.25730652 1. ]]
The correlation between Parent-Children & Survived is: [[1. 0.08162941]
[0.08162941 1. ]]
The correlation between Sibling-Spouse & Survived is: [[ 1. -0.0353225]
[-0.0353225 1. ]]
The correlation between Embarked & Survived is: [[ 1. -0.16767531]
[-0.16767531 1. ]]
There should be higher correlation between Survived and [pClass, sex, Sibling-Spouse] and yet the values are really low. I'm new to this so I understand that a simple method is not the best way to find correlations but at the moment, this doesn't add up.
This is my full code (without the printf() calls):
import pandas as pd
import numpy as np
train = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/test.csv")
survived = train['Survived']
pClass = train['Pclass']
sex = train['Sex'].replace(['female', 'male'], [0, 1])
age = train['Age'].fillna(round(float(np.mean(train['Age'].dropna()))))
fare = train['Fare']
parch = train['Parch']
sibSp = train['SibSp']
embarked = train['Embarked'].replace(['C', 'Q', 'S'], [1, 2, 3])
machine-learning python feature-selection numpy kaggle
edited May 14 at 13:45
Juan Esteban de la Calle
1,363324
asked May 14 at 9:45
Atilla AdrianopolosAtilla Adrianopolos
1134
$endgroup$
add a comment |
$begingroup$
I am currently working through Kaggle's titanic competition and I'm trying to figure out the correlation between the Survived column and other columns. I am using numpy.corrcoef() to matrix the correlation between the columns and here is what I have:
The correlation between pClass & Survived is: [[ 1. -0.33848104]
[-0.33848104 1. ]]
The correlation between Sex & Survived is: [[ 1. -0.54335138]
[-0.54335138 1. ]]
The correlation between Age & Survived is:[[ 1. -0.07065723]
[-0.07065723 1. ]]
The correlation between Fare & Survived is: [[1. 0.25730652]
[0.25730652 1. ]]
The correlation between Parent-Children & Survived is: [[1. 0.08162941]
[0.08162941 1. ]]
The correlation between Sibling-Spouse & Survived is: [[ 1. -0.0353225]
[-0.0353225 1. ]]
The correlation between Embarked & Survived is: [[ 1. -0.16767531]
[-0.16767531 1. ]]
There should be higher correlation between Survived and [pClass, sex, Sibling-Spouse] and yet the values are really low. I'm new to this so I understand that a simple method is not the best way to find correlations but at the moment, this doesn't add up.
This is my full code (without the printf() calls):
import pandas as pd
import numpy as np
train = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/test.csv")
survived = train['Survived']
pClass = train['Pclass']
sex = train['Sex'].replace(['female', 'male'], [0, 1])
age = train['Age'].fillna(round(float(np.mean(train['Age'].dropna()))))
fare = train['Fare']
parch = train['Parch']
sibSp = train['SibSp']
embarked = train['Embarked'].replace(['C', 'Q', 'S'], [1, 2, 3])
machine-learning python feature-selection numpy kaggle
edited May 14 at 13:45
Juan Esteban de la Calle
1,363324
asked May 14 at 9:45
Atilla AdrianopolosAtilla Adrianopolos
1134
$endgroup$
$begingroup$
why do you think the values should be higher?
$endgroup$
– nairboon
May 14 at 9:56
$begingroup$
Because there is a strong correlation between sex, class and survival. Women and rich passengers were most likely to survive.
$endgroup$
– Atilla Adrianopolos
May 14 at 9:59
add a comment |
$begingroup$
I am currently working through Kaggle's titanic competition and I'm trying to figure out the correlation between the Survived column and other columns. I am using numpy.corrcoef() to matrix the correlation between the columns and here is what I have:
The correlation between pClass & Survived is: [[ 1. -0.33848104]
[-0.33848104 1. ]]
The correlation between Sex & Survived is: [[ 1. -0.54335138]
[-0.54335138 1. ]]
The correlation between Age & Survived is:[[ 1. -0.07065723]
[-0.07065723 1. ]]
The correlation between Fare & Survived is: [[1. 0.25730652]
[0.25730652 1. ]]
The correlation between Parent-Children & Survived is: [[1. 0.08162941]
[0.08162941 1. ]]
The correlation between Sibling-Spouse & Survived is: [[ 1. -0.0353225]
[-0.0353225 1. ]]
The correlation between Embarked & Survived is: [[ 1. -0.16767531]
[-0.16767531 1. ]]
There should be higher correlation between Survived and [pClass, sex, Sibling-Spouse] and yet the values are really low. I'm new to this so I understand that a simple method is not the best way to find correlations but at the moment, this doesn't add up.
This is my full code (without the printf() calls):
import pandas as pd
import numpy as np
train = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/test.csv")
survived = train['Survived']
pClass = train['Pclass']
sex = train['Sex'].replace(['female', 'male'], [0, 1])
age = train['Age'].fillna(round(float(np.mean(train['Age'].dropna()))))
fare = train['Fare']
parch = train['Parch']
sibSp = train['SibSp']
embarked = train['Embarked'].replace(['C', 'Q', 'S'], [1, 2, 3])
machine-learning python feature-selection numpy kaggle
edited May 14 at 13:45
Juan Esteban de la Calle
1,363324
asked May 14 at 9:45
Atilla AdrianopolosAtilla Adrianopolos
1134
$endgroup$
I am currently working through Kaggle's titanic competition and I'm trying to figure out the correlation between the Survived column and other columns. I am using numpy.corrcoef() to matrix the correlation between the columns and here is what I have:
The correlation between pClass & Survived is: [[ 1. -0.33848104]
[-0.33848104 1. ]]
The correlation between Sex & Survived is: [[ 1. -0.54335138]
[-0.54335138 1. ]]
The correlation between Age & Survived is:[[ 1. -0.07065723]
[-0.07065723 1. ]]
The correlation between Fare & Survived is: [[1. 0.25730652]
[0.25730652 1. ]]
The correlation between Parent-Children & Survived is: [[1. 0.08162941]
[0.08162941 1. ]]
The correlation between Sibling-Spouse & Survived is: [[ 1. -0.0353225]
[-0.0353225 1. ]]
The correlation between Embarked & Survived is: [[ 1. -0.16767531]
[-0.16767531 1. ]]
There should be higher correlation between Survived and [pClass, sex, Sibling-Spouse] and yet the values are really low. I'm new to this so I understand that a simple method is not the best way to find correlations but at the moment, this doesn't add up.
This is my full code (without the printf() calls):
import pandas as pd
import numpy as np
train = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/test.csv")
survived = train['Survived']
pClass = train['Pclass']
sex = train['Sex'].replace(['female', 'male'], [0, 1])
age = train['Age'].fillna(round(float(np.mean(train['Age'].dropna()))))
fare = train['Fare']
parch = train['Parch']
sibSp = train['SibSp']
embarked = train['Embarked'].replace(['C', 'Q', 'S'], [1, 2, 3])
machine-learning python feature-selection numpy kaggle
machine-learning python feature-selection numpy kaggle
edited May 14 at 13:45
Juan Esteban de la Calle
1,363324
asked May 14 at 9:45
Atilla AdrianopolosAtilla Adrianopolos
1134
edited May 14 at 13:45
Juan Esteban de la Calle
1,363324
asked May 14 at 9:45
Atilla AdrianopolosAtilla Adrianopolos
1134
edited May 14 at 13:45
Juan Esteban de la Calle
1,363324
edited May 14 at 13:45
Juan Esteban de la Calle
1,363324
edited May 14 at 13:45
Juan Esteban de la Calle
1,363324
1,363324
asked May 14 at 9:45
Atilla AdrianopolosAtilla Adrianopolos
1134
asked May 14 at 9:45
Atilla AdrianopolosAtilla Adrianopolos
1134
asked May 14 at 9:45
Atilla AdrianopolosAtilla Adrianopolos
1134
1134
$begingroup$
why do you think the values should be higher?
$endgroup$
– nairboon
May 14 at 9:56
$begingroup$
Because there is a strong correlation between sex, class and survival. Women and rich passengers were most likely to survive.
$endgroup$
– Atilla Adrianopolos
May 14 at 9:59
add a comment |
$begingroup$
why do you think the values should be higher?
$endgroup$
– nairboon
May 14 at 9:56
$begingroup$
Because there is a strong correlation between sex, class and survival. Women and rich passengers were most likely to survive.
$endgroup$
– Atilla Adrianopolos
May 14 at 9:59
$begingroup$
why do you think the values should be higher?
$endgroup$
– nairboon
May 14 at 9:56
$begingroup$
why do you think the values should be higher?
$endgroup$
– nairboon
May 14 at 9:56
$begingroup$
Because there is a strong correlation between sex, class and survival. Women and rich passengers were most likely to survive.
$endgroup$
– Atilla Adrianopolos
May 14 at 9:59
$begingroup$
Because there is a strong correlation between sex, class and survival. Women and rich passengers were most likely to survive.
$endgroup$
– Atilla Adrianopolos
May 14 at 9:59
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
On a side note, I don't think correlation is the correct measure of relation for you to be using, since Survived is technically a binary categorical variable.
"Correlation" measures used should depend on the type of variables being investigated:
- continuous variable v continuous variable: use "traditional" correlation - e.g. Spearman's rank correlation or Pearson's linear correlation.
- continuous variable v categorical variable: use an ANOVA F-test / difference of means
- categorical variable v categorical variable: use Chi-square / Cramer's V
answered May 14 at 11:07
bradSbradS
783214
$endgroup$
1
$begingroup$
Here is a closely related old post.
$endgroup$
– Esmailian
May 18 at 15:29
$begingroup$
@bradS When you sayANOVA F-test/difference of means, do you mean dividing ANOVA F-test by difference of means?
$endgroup$
– Atilla Adrianopolos
May 19 at 17:50
$begingroup$
@AtillaAdrianopolos, no I mean "/" as "or". Using item 3 above as an example, use Chi-square test of independence or Cramer's V.
$endgroup$
– bradS
May 20 at 8:09
add a comment |
$begingroup$
You probably encoded Women as 0 and men as 1 that's why you get a negative correlation of -0.54, because Survived is 0 for No and 1 for Yes. Your calculation actually show what you've expected. The negative correlation is only about the direction depending on your encoding, the relationship between Women and Survived is 0.54.
Similarly pClass is correlated negatively with -0.33 because the highest class (1st class) is encoded as 1 and the lowest as 3, thus the direction is negative.
You could make the relations more intuitive if you make new columns for men and women where you put 0 and 1 depending on the sex, then the correlations will have the intuitive direction (sign). The same holds for pClass.
edited May 14 at 13:16
Stephen Rauch♦
1,51361330
answered May 14 at 10:10
nairboonnairboon
1132
$endgroup$
$begingroup$
I've added my code.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:14
$begingroup$
What if I encodemale/femalewith3/4instead? They're still binary values and just might solve the problem you're raisng.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:15
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
On a side note, I don't think correlation is the correct measure of relation for you to be using, since Survived is technically a binary categorical variable.
"Correlation" measures used should depend on the type of variables being investigated:
- continuous variable v continuous variable: use "traditional" correlation - e.g. Spearman's rank correlation or Pearson's linear correlation.
- continuous variable v categorical variable: use an ANOVA F-test / difference of means
- categorical variable v categorical variable: use Chi-square / Cramer's V
answered May 14 at 11:07
bradSbradS
783214
$endgroup$
1
$begingroup$
Here is a closely related old post.
$endgroup$
– Esmailian
May 18 at 15:29
$begingroup$
@bradS When you sayANOVA F-test/difference of means, do you mean dividing ANOVA F-test by difference of means?
$endgroup$
– Atilla Adrianopolos
May 19 at 17:50
$begingroup$
@AtillaAdrianopolos, no I mean "/" as "or". Using item 3 above as an example, use Chi-square test of independence or Cramer's V.
$endgroup$
– bradS
May 20 at 8:09
add a comment |
$begingroup$
On a side note, I don't think correlation is the correct measure of relation for you to be using, since Survived is technically a binary categorical variable.
"Correlation" measures used should depend on the type of variables being investigated:
- continuous variable v continuous variable: use "traditional" correlation - e.g. Spearman's rank correlation or Pearson's linear correlation.
- continuous variable v categorical variable: use an ANOVA F-test / difference of means
- categorical variable v categorical variable: use Chi-square / Cramer's V
answered May 14 at 11:07
bradSbradS
783214
$endgroup$
1
$begingroup$
Here is a closely related old post.
$endgroup$
– Esmailian
May 18 at 15:29
$begingroup$
@bradS When you sayANOVA F-test/difference of means, do you mean dividing ANOVA F-test by difference of means?
$endgroup$
– Atilla Adrianopolos
May 19 at 17:50
$begingroup$
@AtillaAdrianopolos, no I mean "/" as "or". Using item 3 above as an example, use Chi-square test of independence or Cramer's V.
$endgroup$
– bradS
May 20 at 8:09
add a comment |
$begingroup$
On a side note, I don't think correlation is the correct measure of relation for you to be using, since Survived is technically a binary categorical variable.
"Correlation" measures used should depend on the type of variables being investigated:
- continuous variable v continuous variable: use "traditional" correlation - e.g. Spearman's rank correlation or Pearson's linear correlation.
- continuous variable v categorical variable: use an ANOVA F-test / difference of means
- categorical variable v categorical variable: use Chi-square / Cramer's V
answered May 14 at 11:07
bradSbradS
783214
$endgroup$
On a side note, I don't think correlation is the correct measure of relation for you to be using, since Survived is technically a binary categorical variable.
"Correlation" measures used should depend on the type of variables being investigated:
- continuous variable v continuous variable: use "traditional" correlation - e.g. Spearman's rank correlation or Pearson's linear correlation.
- continuous variable v categorical variable: use an ANOVA F-test / difference of means
- categorical variable v categorical variable: use Chi-square / Cramer's V
answered May 14 at 11:07
bradSbradS
783214
answered May 14 at 11:07
bradSbradS
783214
answered May 14 at 11:07
bradSbradS
783214
answered May 14 at 11:07
bradSbradS
783214
783214
1
$begingroup$
Here is a closely related old post.
$endgroup$
– Esmailian
May 18 at 15:29
$begingroup$
@bradS When you sayANOVA F-test/difference of means, do you mean dividing ANOVA F-test by difference of means?
$endgroup$
– Atilla Adrianopolos
May 19 at 17:50
$begingroup$
@AtillaAdrianopolos, no I mean "/" as "or". Using item 3 above as an example, use Chi-square test of independence or Cramer's V.
$endgroup$
– bradS
May 20 at 8:09
add a comment |
1
$begingroup$
Here is a closely related old post.
$endgroup$
– Esmailian
May 18 at 15:29
$begingroup$
@bradS When you sayANOVA F-test/difference of means, do you mean dividing ANOVA F-test by difference of means?
$endgroup$
– Atilla Adrianopolos
May 19 at 17:50
$begingroup$
@AtillaAdrianopolos, no I mean "/" as "or". Using item 3 above as an example, use Chi-square test of independence or Cramer's V.
$endgroup$
– bradS
May 20 at 8:09
1
1
$begingroup$
Here is a closely related old post.
$endgroup$
– Esmailian
May 18 at 15:29
$begingroup$
Here is a closely related old post.
$endgroup$
– Esmailian
May 18 at 15:29
$begingroup$
@bradS When you say
ANOVA F-test/difference of means, do you mean dividing ANOVA F-test by difference of means?$endgroup$
– Atilla Adrianopolos
May 19 at 17:50
$begingroup$
@bradS When you say
ANOVA F-test/difference of means, do you mean dividing ANOVA F-test by difference of means?$endgroup$
– Atilla Adrianopolos
May 19 at 17:50
$begingroup$
@AtillaAdrianopolos, no I mean "/" as "or". Using item 3 above as an example, use Chi-square test of independence or Cramer's V.
$endgroup$
– bradS
May 20 at 8:09
$begingroup$
@AtillaAdrianopolos, no I mean "/" as "or". Using item 3 above as an example, use Chi-square test of independence or Cramer's V.
$endgroup$
– bradS
May 20 at 8:09
add a comment |
$begingroup$
You probably encoded Women as 0 and men as 1 that's why you get a negative correlation of -0.54, because Survived is 0 for No and 1 for Yes. Your calculation actually show what you've expected. The negative correlation is only about the direction depending on your encoding, the relationship between Women and Survived is 0.54.
Similarly pClass is correlated negatively with -0.33 because the highest class (1st class) is encoded as 1 and the lowest as 3, thus the direction is negative.
You could make the relations more intuitive if you make new columns for men and women where you put 0 and 1 depending on the sex, then the correlations will have the intuitive direction (sign). The same holds for pClass.
edited May 14 at 13:16
Stephen Rauch♦
1,51361330
answered May 14 at 10:10
nairboonnairboon
1132
$endgroup$
$begingroup$
I've added my code.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:14
$begingroup$
What if I encodemale/femalewith3/4instead? They're still binary values and just might solve the problem you're raisng.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:15
add a comment |
$begingroup$
You probably encoded Women as 0 and men as 1 that's why you get a negative correlation of -0.54, because Survived is 0 for No and 1 for Yes. Your calculation actually show what you've expected. The negative correlation is only about the direction depending on your encoding, the relationship between Women and Survived is 0.54.
Similarly pClass is correlated negatively with -0.33 because the highest class (1st class) is encoded as 1 and the lowest as 3, thus the direction is negative.
You could make the relations more intuitive if you make new columns for men and women where you put 0 and 1 depending on the sex, then the correlations will have the intuitive direction (sign). The same holds for pClass.
edited May 14 at 13:16
Stephen Rauch♦
1,51361330
answered May 14 at 10:10
nairboonnairboon
1132
$endgroup$
$begingroup$
I've added my code.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:14
$begingroup$
What if I encodemale/femalewith3/4instead? They're still binary values and just might solve the problem you're raisng.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:15
add a comment |
$begingroup$
You probably encoded Women as 0 and men as 1 that's why you get a negative correlation of -0.54, because Survived is 0 for No and 1 for Yes. Your calculation actually show what you've expected. The negative correlation is only about the direction depending on your encoding, the relationship between Women and Survived is 0.54.
Similarly pClass is correlated negatively with -0.33 because the highest class (1st class) is encoded as 1 and the lowest as 3, thus the direction is negative.
You could make the relations more intuitive if you make new columns for men and women where you put 0 and 1 depending on the sex, then the correlations will have the intuitive direction (sign). The same holds for pClass.
edited May 14 at 13:16
Stephen Rauch♦
1,51361330
answered May 14 at 10:10
nairboonnairboon
1132
$endgroup$
You probably encoded Women as 0 and men as 1 that's why you get a negative correlation of -0.54, because Survived is 0 for No and 1 for Yes. Your calculation actually show what you've expected. The negative correlation is only about the direction depending on your encoding, the relationship between Women and Survived is 0.54.
Similarly pClass is correlated negatively with -0.33 because the highest class (1st class) is encoded as 1 and the lowest as 3, thus the direction is negative.
You could make the relations more intuitive if you make new columns for men and women where you put 0 and 1 depending on the sex, then the correlations will have the intuitive direction (sign). The same holds for pClass.
edited May 14 at 13:16
Stephen Rauch♦
1,51361330
answered May 14 at 10:10
nairboonnairboon
1132
edited May 14 at 13:16
Stephen Rauch♦
1,51361330
edited May 14 at 13:16
Stephen Rauch♦
1,51361330
edited May 14 at 13:16
Stephen Rauch♦
1,51361330
1,51361330
answered May 14 at 10:10
nairboonnairboon
1132
answered May 14 at 10:10
nairboonnairboon
1132
answered May 14 at 10:10
nairboonnairboon
1132
1132
$begingroup$
I've added my code.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:14
$begingroup$
What if I encodemale/femalewith3/4instead? They're still binary values and just might solve the problem you're raisng.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:15
add a comment |
$begingroup$
I've added my code.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:14
$begingroup$
What if I encodemale/femalewith3/4instead? They're still binary values and just might solve the problem you're raisng.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:15
$begingroup$
I've added my code.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:14
$begingroup$
I've added my code.
$endgroup$
– Atilla Adrianopolos
May 14 at 10:14
$begingroup$
What if I encode
male/female with 3/4 instead? They're still binary values and just might solve the problem you're raisng.$endgroup$
– Atilla Adrianopolos
May 14 at 10:15
$begingroup$
What if I encode
male/female with 3/4 instead? They're still binary values and just might solve the problem you're raisng.$endgroup$
– Atilla Adrianopolos
May 14 at 10:15
add a comment |
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$begingroup$
why do you think the values should be higher?
$endgroup$
– nairboon
May 14 at 9:56
$begingroup$
Because there is a strong correlation between sex, class and survival. Women and rich passengers were most likely to survive.
$endgroup$
– Atilla Adrianopolos
May 14 at 9:59