Toros91
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What is the difference between cross_validate and cross_val_score?
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18 votes

cross_val_score is a helper function on the estimator and the dataset. Would explain it with an example: >>> from sklearn.model_selection import cross_val_score >>> clf = svm.SVC(...

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Taking average of multiple neural networks?
6 votes

I think even this method is also called Ensemble Method. How could I conclude that? You might have heard about this algorithm named Random Forest, what does it do? It take data randomly at row level ...

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Predict task duration
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6 votes

I think the analysis which you have done was good. Regarding the Survival Analysis procedure, I think using it in your scenario is good enough. Even it might take time but the results from that are ...

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Use cases for graph algorithms and graph data structures in finance and banking
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5 votes

There are many use cases of graph theory in Finance industry and it is a very broad question. As Emre said can be used for Fraud Detection, Risk Modelling, Economic Networks etc. These below links ...

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Evaluation metrics for Decision Tree regressor and KNN regressor
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4 votes

Generally when ever we are trying to compare between models and to choose the best one, we go for other metrics like AIC, BIC, AUC(this is not applicable as it is used for classification algorithm) ...

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Feature selection vs Feature extraction. Which to use when?
4 votes

I think they are 2 different things, Lets start with Feature Selection: This technique is used for selecting the features which explain the most of the target variable(has a correlation with the ...

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What to report in the build model, asses model and evaluate results steps of CRISP-DM?
3 votes

The way you are trying to present the outcome is pretty good. I cannot say that the following procedure is the standard procedure in my scenario I did something like this: This is how I presented to ...

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is it possible to do feature selection for unsupervised machine learning problems?
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3 votes

Feature Selection is a technique which is used when we you know the target variable(Supervised Learning) When we talk with respect to Unsupervised Learning, there is no exact technique which could do ...

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What makes you confident in your results? At what point do you think you can present your work to tech illiterate superiors?
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3 votes

Hey Welcome to the Site! What you are saying is right, Data Science din't reach to the stage where it has some standard methods for achieving this(standard procedures, don't know we would be able to ...

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How to compute precision and accuracy of a sequence that is not strictly binary?
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3 votes

Welcome to the Site! We know that this problem is Multi-Class Classification Problem. To get a confusion matrix for the same you can use the following command: from mlxtend.evaluate import ...

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Adding feature leads to worse results
3 votes

Welcome to the site! If I understand your question correctly you want to know why a model would perform worse when a new feature is added? So every time you do feature engineering (add new columns, ...

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Using machine learning technique to predict commodity prices
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3 votes

Based on your question there are couple of things which I would assume to answer your question: As you need to predict the commodity price the data which is collected is time series data. Since you ...

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Text post-processing
3 votes

Yes, you can do that by add them to the existing NLTK Stop-word dictionary for all such words/Creating a Custom Stop-word dictionary. for Custom Stop-word dictionary you need to include all the key ...

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Ensemble Probabilities of the different models
3 votes

I think it can be done by using this command at the time of prediction, giving example in R #To predict with probabilities testSet$pred_rf_prob<-`predict(object,model_rf,testSet[,predictors],type='...

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Classification based on a Clustering Result
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3 votes

I think you need to do some Feature Engineering, i.e., as you explained in the question, those values mean something to your application. For example : 1-3 : Bad, 4-6 : Average, 7-10 : Good V1 ...

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Clustering with cosine similarity
3 votes

I think the clustMixType package might give you better results/insights. By using this package you can use combination of Categorical and Numeric Data directly, it doesn’t need any kind of hot ...

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RF and DT overfitting
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2 votes

I think you can perform Predictor Importance test and see which are the variable explaining the most. There is this package named Boruta, you can go through the link for implementation in python. ...

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How to do Feature Scaling for these ranges [0,1] and [-1,1]?
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2 votes

What you said is right, the above equation is for normalizing the data with-in the range of [0,1] Now, we can generalize using the below equation To normalize in $[-1,1]$ you can use: $$ x'' = 2\...

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Does ensemble (bagging, boosting, stacking, etc) always at least increase performance?
2 votes

Under Ensemble you can use Majority Votes, Average, Weights etc to get the final outcome from Ensemble model. To understand it better you can go through this Link, explained well by Alexander. Now, ...

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Choice of time series models
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2 votes

First thing first, when ever you use Time Series data you call it as Forecasting not Prediction as it is time dependent. To understand why you can go through this link Metrics to compare models When ...

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Removing Categorial Features in Linear Regression
2 votes

I think using Linear Regression is not a good option as, This performs very well on numeric variables(categorical -> binary). Cannot handle Missing Data(suggestible to ignore those records). When ...

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Beginner math books for Machine Learning
2 votes

Before doing my master in Analytics, I was suggested by my seniors to go through these couple of books to know more about Machine Learning and Statistics. Namely: Discovering statistics with SPSS/R -...

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Imputing for multiple missing variables using sklearn
2 votes

Welcome to the site! If I understand your question correctly you mean to say that you are facing issue with replacing the missing values. Firstly, we cannot use the Same Technique to replace missing ...

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How to predict a group of events using machine learning?
2 votes

Welcome to the site! Assumptions before answering your question: The Target variable is a categorical(each stage is considered as a separate category). You have the past data of the customers whose ...

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What is training
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2 votes

Welcome to the Site! Assuming that simple neural network means Single layer Perceptron. I think you need to understand this, a simple neural network with a single hidden layer cannot solve an XOR ...

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Correlation and feature selection
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2 votes

As we were discussing above regarding the correlation, yes it is very important factor which would play an important role in selecting the features which are useful in explaining the Target Variable. ...

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Best methods to solve class imbalance problem and why?
2 votes

There are 2 Techniques: Oversampling: There are many techniques under this, ROSE and SMOTE are the most famous techniques used for oversampling. In ROSE it just increases the minority classes. In ...

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When to use Linear Regression and When to use Logistic regression - use cases
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2 votes

Logistic Regression is used when you know that the data is lineraly seperable/classifiable and the outcome is Binary or Dichotomous but it can extended when the dependent has more than 2 categories. ...

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Kmodes for Mixed Data
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2 votes

As far as I remember Kmodes is used for Categorical data, even in the documentation I couldn't find anything related to Mixed Data Type, if you have some reference do share. I've used Kproto for Mixed ...

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Forecast Model recognize future trend
2 votes

Before going into modelling, I guess you can do a bit more of exploratory analysis(month by month, year by year). If you find any trend or seasonality and so on. Why did you go to RF directly without ...

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