Feature importance determination and correlation
I want to know which of my varibles have the strongest effect on SalePrice
in my DataFrame df_train.
Id MSSubClass MSZoning ... SaleType SaleCondition SalePrice
0 1 60 RL ... WD Normal 208500
1 2 20 RL ... WD Normal 181500
2 3 60 RL ... WD Normal 223500
3 4 70 RL ... WD Abnorml 140000
4 5 60 RL ... WD Normal 250000
For this purpose, I have analized correlation,as well as feature_importances_ of sklearn.
The code for correlation and visualization, with heatmap, is:
corrmat = df_train.corr()
k = 20 #number of variables for heatmap
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(df_train[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
And for feature importance determination is:
feature_labels = np.array(['OverallQual', 'GrLivArea', 'SimplOverallQual', 'ExterQual', 'GarageCars', 'KitchenQual', 'SimplExterQual', 'GarageArea', 'SimplKitchenQual', 'TotalBsmtSF', 'FullBath', 'YearBuilt', '1stFlrSF', 'YearRemodAdd', 'TotRmsAbvGrd', 'Fireplaces', 'HeatingQC', 'LotArea', 'MasVnrArea']) importance = model.feature_importances_ feature_indexes_by_importance = importance.argsort()
indices = np.argsort(importance)[::-1] for index in feature_indexes_by_importance:
print('{}-{:.2f}%'.format(feature_labels[index], (importance[index] *100.0)))
'OverallQual', 'GrLivArea' and 'SimplQual'are the most correlated variables with SalePrice according to heatmap.
And according to feature importance most important ones are:
GarageArea-9.71%
GrLivArea-15.43%
LotArea-17.46%
What is the problem that could explain why correlation and feature_importances_ of sklearn don´t correlate?
Thanks
python heatmap correlation feature-selection
asked Nov 22 at 18:01
Ley
193
add a comment |
I want to know which of my varibles have the strongest effect on SalePrice
in my DataFrame df_train.
Id MSSubClass MSZoning ... SaleType SaleCondition SalePrice
0 1 60 RL ... WD Normal 208500
1 2 20 RL ... WD Normal 181500
2 3 60 RL ... WD Normal 223500
3 4 70 RL ... WD Abnorml 140000
4 5 60 RL ... WD Normal 250000
For this purpose, I have analized correlation,as well as feature_importances_ of sklearn.
The code for correlation and visualization, with heatmap, is:
corrmat = df_train.corr()
k = 20 #number of variables for heatmap
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(df_train[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
And for feature importance determination is:
feature_labels = np.array(['OverallQual', 'GrLivArea', 'SimplOverallQual', 'ExterQual', 'GarageCars', 'KitchenQual', 'SimplExterQual', 'GarageArea', 'SimplKitchenQual', 'TotalBsmtSF', 'FullBath', 'YearBuilt', '1stFlrSF', 'YearRemodAdd', 'TotRmsAbvGrd', 'Fireplaces', 'HeatingQC', 'LotArea', 'MasVnrArea']) importance = model.feature_importances_ feature_indexes_by_importance = importance.argsort()
indices = np.argsort(importance)[::-1] for index in feature_indexes_by_importance:
print('{}-{:.2f}%'.format(feature_labels[index], (importance[index] *100.0)))
'OverallQual', 'GrLivArea' and 'SimplQual'are the most correlated variables with SalePrice according to heatmap.
And according to feature importance most important ones are:
GarageArea-9.71%
GrLivArea-15.43%
LotArea-17.46%
What is the problem that could explain why correlation and feature_importances_ of sklearn don´t correlate?
Thanks
python heatmap correlation feature-selection
asked Nov 22 at 18:01
Ley
193
How are these features correlated among themselves?
– Vivek Kumar
Nov 23 at 8:41
add a comment |
I want to know which of my varibles have the strongest effect on SalePrice
in my DataFrame df_train.
Id MSSubClass MSZoning ... SaleType SaleCondition SalePrice
0 1 60 RL ... WD Normal 208500
1 2 20 RL ... WD Normal 181500
2 3 60 RL ... WD Normal 223500
3 4 70 RL ... WD Abnorml 140000
4 5 60 RL ... WD Normal 250000
For this purpose, I have analized correlation,as well as feature_importances_ of sklearn.
The code for correlation and visualization, with heatmap, is:
corrmat = df_train.corr()
k = 20 #number of variables for heatmap
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(df_train[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
And for feature importance determination is:
feature_labels = np.array(['OverallQual', 'GrLivArea', 'SimplOverallQual', 'ExterQual', 'GarageCars', 'KitchenQual', 'SimplExterQual', 'GarageArea', 'SimplKitchenQual', 'TotalBsmtSF', 'FullBath', 'YearBuilt', '1stFlrSF', 'YearRemodAdd', 'TotRmsAbvGrd', 'Fireplaces', 'HeatingQC', 'LotArea', 'MasVnrArea']) importance = model.feature_importances_ feature_indexes_by_importance = importance.argsort()
indices = np.argsort(importance)[::-1] for index in feature_indexes_by_importance:
print('{}-{:.2f}%'.format(feature_labels[index], (importance[index] *100.0)))
'OverallQual', 'GrLivArea' and 'SimplQual'are the most correlated variables with SalePrice according to heatmap.
And according to feature importance most important ones are:
GarageArea-9.71%
GrLivArea-15.43%
LotArea-17.46%
What is the problem that could explain why correlation and feature_importances_ of sklearn don´t correlate?
Thanks
python heatmap correlation feature-selection
asked Nov 22 at 18:01
Ley
193
I want to know which of my varibles have the strongest effect on SalePrice
in my DataFrame df_train.
Id MSSubClass MSZoning ... SaleType SaleCondition SalePrice
0 1 60 RL ... WD Normal 208500
1 2 20 RL ... WD Normal 181500
2 3 60 RL ... WD Normal 223500
3 4 70 RL ... WD Abnorml 140000
4 5 60 RL ... WD Normal 250000
For this purpose, I have analized correlation,as well as feature_importances_ of sklearn.
The code for correlation and visualization, with heatmap, is:
corrmat = df_train.corr()
k = 20 #number of variables for heatmap
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(df_train[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
And for feature importance determination is:
feature_labels = np.array(['OverallQual', 'GrLivArea', 'SimplOverallQual', 'ExterQual', 'GarageCars', 'KitchenQual', 'SimplExterQual', 'GarageArea', 'SimplKitchenQual', 'TotalBsmtSF', 'FullBath', 'YearBuilt', '1stFlrSF', 'YearRemodAdd', 'TotRmsAbvGrd', 'Fireplaces', 'HeatingQC', 'LotArea', 'MasVnrArea']) importance = model.feature_importances_ feature_indexes_by_importance = importance.argsort()
indices = np.argsort(importance)[::-1] for index in feature_indexes_by_importance:
print('{}-{:.2f}%'.format(feature_labels[index], (importance[index] *100.0)))
'OverallQual', 'GrLivArea' and 'SimplQual'are the most correlated variables with SalePrice according to heatmap.
And according to feature importance most important ones are:
GarageArea-9.71%
GrLivArea-15.43%
LotArea-17.46%
What is the problem that could explain why correlation and feature_importances_ of sklearn don´t correlate?
Thanks
python heatmap correlation feature-selection
python heatmap correlation feature-selection
asked Nov 22 at 18:01
Ley
193
asked Nov 22 at 18:01
Ley
193
asked Nov 22 at 18:01
Ley
193
asked Nov 22 at 18:01
Ley
193
asked Nov 22 at 18:01
Ley
193
193
How are these features correlated among themselves?
– Vivek Kumar
Nov 23 at 8:41
add a comment |
How are these features correlated among themselves?
– Vivek Kumar
Nov 23 at 8:41
How are these features correlated among themselves?
– Vivek Kumar
Nov 23 at 8:41
How are these features correlated among themselves?
– Vivek Kumar
Nov 23 at 8:41
add a comment |
1 Answer
1
active
oldest
votes
I suppose you are talking about forest of trees feature_importances_? (https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html)
Correlation measures a linear correlation between the features and your output, random forest use non linear classification that have nothing to do with linear correlation, and will be able to extract the features that non linearly have the most importance in the task.
answered Nov 22 at 18:05
Matthieu Brucher
11.8k22137
add a comment |
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I suppose you are talking about forest of trees feature_importances_? (https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html)
Correlation measures a linear correlation between the features and your output, random forest use non linear classification that have nothing to do with linear correlation, and will be able to extract the features that non linearly have the most importance in the task.
answered Nov 22 at 18:05
Matthieu Brucher
11.8k22137
add a comment |
I suppose you are talking about forest of trees feature_importances_? (https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html)
Correlation measures a linear correlation between the features and your output, random forest use non linear classification that have nothing to do with linear correlation, and will be able to extract the features that non linearly have the most importance in the task.
answered Nov 22 at 18:05
Matthieu Brucher
11.8k22137
add a comment |
I suppose you are talking about forest of trees feature_importances_? (https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html)
Correlation measures a linear correlation between the features and your output, random forest use non linear classification that have nothing to do with linear correlation, and will be able to extract the features that non linearly have the most importance in the task.
answered Nov 22 at 18:05
Matthieu Brucher
11.8k22137
I suppose you are talking about forest of trees feature_importances_? (https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html)
Correlation measures a linear correlation between the features and your output, random forest use non linear classification that have nothing to do with linear correlation, and will be able to extract the features that non linearly have the most importance in the task.
answered Nov 22 at 18:05
Matthieu Brucher
11.8k22137
answered Nov 22 at 18:05
Matthieu Brucher
11.8k22137
answered Nov 22 at 18:05
Matthieu Brucher
11.8k22137
answered Nov 22 at 18:05
Matthieu Brucher
11.8k22137
11.8k22137
add a comment |
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How are these features correlated among themselves?
– Vivek Kumar
Nov 23 at 8:41