Does integer encoding of strings and using this as an input to decision tree (sklearn) makes the splitting attributes discrete or continuous?Passing categorical data to Sklearn Decision TreeHow to handle catagorical data while training decision tree using scikit-learn/ sklearn?How to explain feature importance after one-hot encode used for decision treeIs there any way to visualize decision tree (sklearn) with categorical features consolidated from one hot encoded features?Discretizing continuous variables for RandomForest in SklearnUse of one-hot encoder to build decision treesIn sklearn, how can one-hot encoding help when building decision tree with categorical features?Using OneHotEncoder for categorical features in decision tree classifierDecision Tree producing 0.5 value at split for binary variablesHow to use unified pipelines on numerical and categorical features in machine learning?How do you feed 'str' data to decision tree without one-hot encoding
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Does integer encoding of strings and using this as an input to decision tree (sklearn) makes the splitting attributes discrete or continuous?
Passing categorical data to Sklearn Decision TreeHow to handle catagorical data while training decision tree using scikit-learn/ sklearn?How to explain feature importance after one-hot encode used for decision treeIs there any way to visualize decision tree (sklearn) with categorical features consolidated from one hot encoded features?Discretizing continuous variables for RandomForest in SklearnUse of one-hot encoder to build decision treesIn sklearn, how can one-hot encoding help when building decision tree with categorical features?Using OneHotEncoder for categorical features in decision tree classifierDecision Tree producing 0.5 value at split for binary variablesHow to use unified pipelines on numerical and categorical features in machine learning?How do you feed 'str' data to decision tree without one-hot encoding
I have to use Decision Tree classifier to classify certain data. However, the attribute values are strings, and as I found here: https://datascience.stackexchange.com/questions/5226/strings-as-features-in-decision-tree-random-forest, it said that strings cannot be used as an input. Hence I used integer encoding for the strings.
In this article, Passing categorical data to Sklearn Decision Tree, I found out that passing integer-encoded data may result in a wrong answer since sklearn assumes an ordering among the data. So, the only way out is using OneHotEncoder module.
Using OneHotEncoder module increases the number of features (e.g. if there is an attribute 'price' with values ['high','med','low'], one-hot-encoding would result in inclusion of 3 attributes related to the actual attribute 'price'; those can be interpreted as ['price-high','price-med', 'price-low'] and the attribute values will be either 1 or 0 depending on the data), which I don't want since I have to print the decision tree in a certain format which would require the original features (e.g. I need 'price').
Is there a way out of this?
python scikit-learn decision-tree
asked Mar 8 at 11:26
Sarthak ChakrabortySarthak Chakraborty
111
add a comment |
I have to use Decision Tree classifier to classify certain data. However, the attribute values are strings, and as I found here: https://datascience.stackexchange.com/questions/5226/strings-as-features-in-decision-tree-random-forest, it said that strings cannot be used as an input. Hence I used integer encoding for the strings.
In this article, Passing categorical data to Sklearn Decision Tree, I found out that passing integer-encoded data may result in a wrong answer since sklearn assumes an ordering among the data. So, the only way out is using OneHotEncoder module.
Using OneHotEncoder module increases the number of features (e.g. if there is an attribute 'price' with values ['high','med','low'], one-hot-encoding would result in inclusion of 3 attributes related to the actual attribute 'price'; those can be interpreted as ['price-high','price-med', 'price-low'] and the attribute values will be either 1 or 0 depending on the data), which I don't want since I have to print the decision tree in a certain format which would require the original features (e.g. I need 'price').
Is there a way out of this?
python scikit-learn decision-tree
asked Mar 8 at 11:26
Sarthak ChakrabortySarthak Chakraborty
111
add a comment |
I have to use Decision Tree classifier to classify certain data. However, the attribute values are strings, and as I found here: https://datascience.stackexchange.com/questions/5226/strings-as-features-in-decision-tree-random-forest, it said that strings cannot be used as an input. Hence I used integer encoding for the strings.
In this article, Passing categorical data to Sklearn Decision Tree, I found out that passing integer-encoded data may result in a wrong answer since sklearn assumes an ordering among the data. So, the only way out is using OneHotEncoder module.
Using OneHotEncoder module increases the number of features (e.g. if there is an attribute 'price' with values ['high','med','low'], one-hot-encoding would result in inclusion of 3 attributes related to the actual attribute 'price'; those can be interpreted as ['price-high','price-med', 'price-low'] and the attribute values will be either 1 or 0 depending on the data), which I don't want since I have to print the decision tree in a certain format which would require the original features (e.g. I need 'price').
Is there a way out of this?
python scikit-learn decision-tree
asked Mar 8 at 11:26
Sarthak ChakrabortySarthak Chakraborty
111
I have to use Decision Tree classifier to classify certain data. However, the attribute values are strings, and as I found here: https://datascience.stackexchange.com/questions/5226/strings-as-features-in-decision-tree-random-forest, it said that strings cannot be used as an input. Hence I used integer encoding for the strings.
In this article, Passing categorical data to Sklearn Decision Tree, I found out that passing integer-encoded data may result in a wrong answer since sklearn assumes an ordering among the data. So, the only way out is using OneHotEncoder module.
Using OneHotEncoder module increases the number of features (e.g. if there is an attribute 'price' with values ['high','med','low'], one-hot-encoding would result in inclusion of 3 attributes related to the actual attribute 'price'; those can be interpreted as ['price-high','price-med', 'price-low'] and the attribute values will be either 1 or 0 depending on the data), which I don't want since I have to print the decision tree in a certain format which would require the original features (e.g. I need 'price').
Is there a way out of this?
python scikit-learn decision-tree
python scikit-learn decision-tree
asked Mar 8 at 11:26
Sarthak ChakrabortySarthak Chakraborty
111
asked Mar 8 at 11:26
Sarthak ChakrabortySarthak Chakraborty
111
asked Mar 8 at 11:26
Sarthak ChakrabortySarthak Chakraborty
111
asked Mar 8 at 11:26
Sarthak ChakrabortySarthak Chakraborty
111
asked Mar 8 at 11:26
Sarthak ChakrabortySarthak Chakraborty
111
111
add a comment |
add a comment |
1 Answer
1
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oldest
votes
I think pd.get_dummies would be useful since you want to keep track of the original feature names, when creating one-hot vectors.
Example:
df = pd.DataFrame('price': ['high', 'medium', 'high', 'low'], 'some_feature': ['b', 'a', 'c','a'])
pd.get_dummies(df,columns=['price','some_feature'])
price_high price_low price_medium some_feature_a some_feature_b some_feature_c
0 1 0 0 0 1 0
1 0 0 1 1 0 0
2 1 0 0 0 0 1
3 0 1 0 1 0 0
When feed this dataframe to decision tree, you could get a better understanding!
answered Mar 9 at 12:38
AI_LearningAI_Learning
4,05021035
Sure. That would convert the data to one-hot-encoded form. But, the Decision Tree will be constructed on the new features (e.g. price_high, price_low, etc). So while printing the Decision Tree, the features would not be "price" or "some_feature", but "price_high", "price_low", etc.
– Sarthak Chakraborty
Mar 10 at 16:42
yes. why do you want the see just thepriceas feature name when we already created dummies for it. I think, having it asprice_highwould have more explanation of how the split has been made in the decision tree
– AI_Learning
Mar 10 at 17:59
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I think pd.get_dummies would be useful since you want to keep track of the original feature names, when creating one-hot vectors.
Example:
df = pd.DataFrame('price': ['high', 'medium', 'high', 'low'], 'some_feature': ['b', 'a', 'c','a'])
pd.get_dummies(df,columns=['price','some_feature'])
price_high price_low price_medium some_feature_a some_feature_b some_feature_c
0 1 0 0 0 1 0
1 0 0 1 1 0 0
2 1 0 0 0 0 1
3 0 1 0 1 0 0
When feed this dataframe to decision tree, you could get a better understanding!
answered Mar 9 at 12:38
AI_LearningAI_Learning
4,05021035
Sure. That would convert the data to one-hot-encoded form. But, the Decision Tree will be constructed on the new features (e.g. price_high, price_low, etc). So while printing the Decision Tree, the features would not be "price" or "some_feature", but "price_high", "price_low", etc.
– Sarthak Chakraborty
Mar 10 at 16:42
yes. why do you want the see just thepriceas feature name when we already created dummies for it. I think, having it asprice_highwould have more explanation of how the split has been made in the decision tree
– AI_Learning
Mar 10 at 17:59
add a comment |
I think pd.get_dummies would be useful since you want to keep track of the original feature names, when creating one-hot vectors.
Example:
df = pd.DataFrame('price': ['high', 'medium', 'high', 'low'], 'some_feature': ['b', 'a', 'c','a'])
pd.get_dummies(df,columns=['price','some_feature'])
price_high price_low price_medium some_feature_a some_feature_b some_feature_c
0 1 0 0 0 1 0
1 0 0 1 1 0 0
2 1 0 0 0 0 1
3 0 1 0 1 0 0
When feed this dataframe to decision tree, you could get a better understanding!
answered Mar 9 at 12:38
AI_LearningAI_Learning
4,05021035
Sure. That would convert the data to one-hot-encoded form. But, the Decision Tree will be constructed on the new features (e.g. price_high, price_low, etc). So while printing the Decision Tree, the features would not be "price" or "some_feature", but "price_high", "price_low", etc.
– Sarthak Chakraborty
Mar 10 at 16:42
yes. why do you want the see just thepriceas feature name when we already created dummies for it. I think, having it asprice_highwould have more explanation of how the split has been made in the decision tree
– AI_Learning
Mar 10 at 17:59
add a comment |
I think pd.get_dummies would be useful since you want to keep track of the original feature names, when creating one-hot vectors.
Example:
df = pd.DataFrame('price': ['high', 'medium', 'high', 'low'], 'some_feature': ['b', 'a', 'c','a'])
pd.get_dummies(df,columns=['price','some_feature'])
price_high price_low price_medium some_feature_a some_feature_b some_feature_c
0 1 0 0 0 1 0
1 0 0 1 1 0 0
2 1 0 0 0 0 1
3 0 1 0 1 0 0
When feed this dataframe to decision tree, you could get a better understanding!
answered Mar 9 at 12:38
AI_LearningAI_Learning
4,05021035
I think pd.get_dummies would be useful since you want to keep track of the original feature names, when creating one-hot vectors.
Example:
df = pd.DataFrame('price': ['high', 'medium', 'high', 'low'], 'some_feature': ['b', 'a', 'c','a'])
pd.get_dummies(df,columns=['price','some_feature'])
price_high price_low price_medium some_feature_a some_feature_b some_feature_c
0 1 0 0 0 1 0
1 0 0 1 1 0 0
2 1 0 0 0 0 1
3 0 1 0 1 0 0
When feed this dataframe to decision tree, you could get a better understanding!
answered Mar 9 at 12:38
AI_LearningAI_Learning
4,05021035
answered Mar 9 at 12:38
AI_LearningAI_Learning
4,05021035
answered Mar 9 at 12:38
AI_LearningAI_Learning
4,05021035
answered Mar 9 at 12:38
AI_LearningAI_Learning
4,05021035
4,05021035
Sure. That would convert the data to one-hot-encoded form. But, the Decision Tree will be constructed on the new features (e.g. price_high, price_low, etc). So while printing the Decision Tree, the features would not be "price" or "some_feature", but "price_high", "price_low", etc.
– Sarthak Chakraborty
Mar 10 at 16:42
yes. why do you want the see just thepriceas feature name when we already created dummies for it. I think, having it asprice_highwould have more explanation of how the split has been made in the decision tree
– AI_Learning
Mar 10 at 17:59
add a comment |
Sure. That would convert the data to one-hot-encoded form. But, the Decision Tree will be constructed on the new features (e.g. price_high, price_low, etc). So while printing the Decision Tree, the features would not be "price" or "some_feature", but "price_high", "price_low", etc.
– Sarthak Chakraborty
Mar 10 at 16:42
yes. why do you want the see just thepriceas feature name when we already created dummies for it. I think, having it asprice_highwould have more explanation of how the split has been made in the decision tree
– AI_Learning
Mar 10 at 17:59
Sure. That would convert the data to one-hot-encoded form. But, the Decision Tree will be constructed on the new features (e.g. price_high, price_low, etc). So while printing the Decision Tree, the features would not be "price" or "some_feature", but "price_high", "price_low", etc.
– Sarthak Chakraborty
Mar 10 at 16:42
Sure. That would convert the data to one-hot-encoded form. But, the Decision Tree will be constructed on the new features (e.g. price_high, price_low, etc). So while printing the Decision Tree, the features would not be "price" or "some_feature", but "price_high", "price_low", etc.
– Sarthak Chakraborty
Mar 10 at 16:42
yes. why do you want the see just the
price as feature name when we already created dummies for it. I think, having it as price_high would have more explanation of how the split has been made in the decision tree– AI_Learning
Mar 10 at 17:59
yes. why do you want the see just the
price as feature name when we already created dummies for it. I think, having it as price_high would have more explanation of how the split has been made in the decision tree– AI_Learning
Mar 10 at 17:59
add a comment |
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