categorical-data

I have a feature set with a corresponding categoricalFeaturesInfo: Map[Int,Int]. However, for the life of me I cannot figure out how I am supposed to get the DecisionTree class to work. It will not accept anything, but a LabeledPoint as data. However, LabeledPoint requires (double, vector) where the vector requires doubles. val LP = featureSet.map(x => LabeledPoint(classMap(x(0)),Vectors.dense(x.tail))) // Run training algorithm to build the model val maxDepth: Int = 3 val isMulticlassWithCategoricalFeatures: Boolean = true val numClassesForClassification: Int = countPossibilities(labelCol)

I have a dataset including categorical variables(binary) and continuous variables. I'm trying to apply a linear regression model for predicting a continuous variable. Can someone please let me know how to check for correlation among the categorical variables and the continuous target variable. Current Code: import pandas as pd df_hosp = pd.read_csv('C:\Users\LAPPY-2\Desktop\LengthOfStay.csv') data = df_hosp[['lengthofstay', 'male', 'female', 'dialysisrenalendstage', 'asthma', \ 'irondef', 'pneum', 'substancedependence', \ 'psychologicaldisordermajor', 'depress', 'psychother', \

I'm having some trouble using coxph(). I've two categorical variables: Sex and Probable Cause, that I want to use as predictor variables. Sex is just the typical male/female but Probable Cause has 5 options. I don't know what is the problem with the warning message. Why does the cofidence intervals are from 0 to Inf and the p-values so high? Here's the code and the output: > my_coxph <- coxph(Surv(tempo,status) ~ factor(Sexo)+ factor(Causa.provavel) , data=ceabn) Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Loglik converged before variable 2,3,5,6 ; beta

I am converting strings to categorical values in my dataset using the following piece of code. data['weekday'] = pd.Categorical.from_array(data.weekday).labels For eg, index weekday 0 Sunday 1 Sunday 2 Wednesday 3 Monday 4 Monday 5 Thursday 6 Tuesday After encoding the weekday, my dataset appears like this: index weekday 0 3 1 3 2 6 3 1 4 1 5 4 6 5 Is there any way I can know that Sunday has been mapped to 3, Wednesday to 6 and so on? The best way of doing this can be to use label encoder of sklearn library. Something like this: from sklearn import preprocessing le = preprocessing.LabelEncoder

The main goals are as follows: 1) Apply StandardScaler to continuous variables 2) Apply LabelEncoder and OnehotEncoder to categorical variables The continuous variables need to be scaled, but at the same time, a couple of categorical variables are also of integer type. Applying StandardScaler would result in undesired effects. On the flip side, the StandardScaler would scale the integer based categorical variables, which is also not we what. Since continuous variables and categorical ones are mixed in a single Pandas DataFrame, what's the recommended workflow to approach this kind of problem?

I have a DataFrame df with a non-numerical column CatColumn . A B CatColumn 0 381.1396 7.343921 Medium 1 481.3268 6.786945 Medium 2 263.3766 7.628746 High 3 177.2400 5.225647 Medium-High I want to include CatColumn in the correlation analysis with other columns in the Dataframe. I tried DataFrame.corr but it does not include columns with nominal values in the correlation analysis. I am going to strongly disagree with the other comments. They miss the main point of correlation: How much does variable 1 increase or decrease as variable 2 increases or decreases. So in the very first place, order