![]() The mapping can be done using the replace() function of a Pandas Series. Some times it will not be obvious, then you must use your business domain knowledge or consult with a business analyst to confirm it. This order must be known to you while converting any ordinal categorical data. Hence, while converting them to numeric, we must assign such numeric values that represent the natural ordering of the variables. Ordinal Variable: Categorical strings which have some natural ordering, for example, the Size column can be ordered automatically like SP2>P3 etc. How to Solve ValueError: could not convert string to float In order to solve it, you can use the try.except block.The predictor variables could be of two types, You can skip the numeric conversion of the string target variable while doing classification, as it is handled by the algorithms. Hence, we need to convert the input data into numeric before passing it on to the algorithms for training. The function can also be applied over multiple columns of a DataFrame using apply. OneHotEncoder, notice how only 2 decisions nodes are needed.Machine learning algorithms do not understand strings. 1 You can use pd.tonumeric (introduced in version 0.17) to convert a column or a Series to a numeric type. If we try to do so for the column - amount: df'amount'.astype(float) we will face error: ValueError: could not convert string to float: '10.00' Step 2: ValueError: Unable to parse string '10. LabelEncoder, notice how 3 decisions nodes are needed. Step 1: ValueError: could not convert string to float To convert string to float we can use the function. float function not allow to convert complex type into float type. Have a look at the tree generated given the two types of input: float() function is used to convert a variable into float type from string ,int or bool. Where as, if you encode with a simple LabelEncoder, you will need to have deeper tree. code snippet convert X into dataframe Xpd pd.DataFrame(dataX) replace all instances of URC with 0 Xreplace Xpd.replace(' ',0, regexTrue) convert it back to numpy array Xnp Xreplace.values set the object type as float. OneHotEncoder from sklearn.preprocessing import OneHotEncoderįor algorithms like DecisionTreeClassifiers, the second option, namely OneHotEncoder is better because there are more dimensions to finding boundary lines. Though not the best solution, I found some success by converting it into pandas dataframe and working along. OrdinalEncoder from sklearn.preprocessing import OrdinalEncoder ![]() The way in which they encode X is different, observe: In Sklearn you can use the OrdinalEncoder ( docs) or the OneHotEncoder ( docs). Im getting the following error: return array(a, dtype, copyFalse, orderorder) ValueError: could not convert string to float: BOX72(BOX72 is a value under. Now, given that you need to convert your string features X into numerals, the way that you convert will affect the algorithm. from sklearn.linearmodel import LinearRegression X X.apply (pd. If theyre incompatible with conversion, theyll be reduced to NaN s. If you don't convert your targets y into integers, there will be no decrease in your algorithms performance. A quick solution would involve using pd.tonumeric to convert whatever strings your data might contain to numeric values. ![]() 2) Will the algorithms work less effectively? This means that features in X must be transformed to integers, however, target labels in y can remain as strings. This transforms your labels into integers. So definitely, you need to convert them to integers, for example: from sklearn.preprocessing import OrdinalEncoder I would suggest you to one hot encode your non-numeric variables. ![]() ValueError: could not convert string to float: 'sunny' sklearn decision tree - could not convert string to float score:0 Accepted answer DecisionTreeClassifier 's fit method takes arrays of float in it's X parameter (documentation). If you try to fit a DecisionTreeClassifier with this, like this: from ee import DecisionTreeClassifier Thats a regression problem, not a classification problem. So, here is some data to cover both cases: X = [ Its looks to me as though youre trying to predict a floating-point value (employment rate). You did not specify if your string labels are one of your features in your feature matrix X, or if they are your target y. 1) Is it necessary to convert the strings to integers? ![]() However, if your labels are just your targets, you can leave them as is. If your labels are part of your feature matrix, you need to convert them to numerals using OneHotEncoding ( docs). ![]()
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