random forest classifier in python

pandas - random forest classifier error with python - index out of bounds - stack overflow

pandas - random forest classifier error with python - index out of bounds - stack overflow

I feel like I know why this happened. They had me drop the target variable, 'hotel_cluster' so it couldn't be included. However, I did this because the link told me to. So when I fix it and keep it in there, the error received is:

I feel like something is either off with that link or wrong with my dataset. I want to stick with this link because it got me so much farther than the others. Here's a screenshot with the head and info. Please let me know if I'm missing anything. Thanks! Head/Info

the method index() of a list object returns the index of the queried element in a list, i.e. an integer (int) value. As a side node: a pandas.DataFrame object would expect a column name, i.e. a string (str). Since you have a matrix (numpy.ndarray), you are required to slice it by integers -- so you're OK. (BTW, pandas.DataFrame have a build-in method for conversion to numpy.ndarrays that is pandas.DataFrame.to_numpy()). If you do not drop the 'hotel_cluster' column from the table features, the converted matrix must have the same width as the original table -- and this also means, the same width as the length of the previously stored list of colum names feature_list. Check:

I guess that you were using a Python interactive console and some bug sneaked there into your code. If you do not drop any column, test_features[:, feature_list.index('hotel_cluster')] should work. (you way want to rerun the code without the dropping line)

Besides this, keep the line when dropping 'hotel_cluster' and replace the indexing line with baseline_preds = test_labels as this seems to be the intention anyway. However, the rest of the code is of no use then and the description in the comments also won't suit a baseline prediction. I guess that you want to do some moving average thing here.

colsing node: Welcome to stackoverflow. The next time, please strip your code to an absolute minimum reproducible example. Random Forest has no relation to you current prolbem (all sklearn imports) and downloading 3 GB of data is most likely not necessary to reproduce your error. It may be beneficial creating a toy example, e.g.

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how to develop a random forest ensemble in python

how to develop a random forest ensemble in python

In bagging, a number of decision trees are created where each tree is created from a different bootstrap sample of the training dataset. A bootstrap sample is a sample of the training dataset where a sample may appear more than once in the sample, referred to as sampling with replacement.

Bagging is an effective ensemble algorithm as each decision tree is fit on a slightly different training dataset, and in turn, has a slightly different performance. Unlike normal decision tree models, such as classification and regression trees (CART), trees used in the ensemble are unpruned, making them slightly overfit to the training dataset. This is desirable as it helps to make each tree more different and have less correlated predictions or prediction errors.

A prediction on a regression problem is the average of the prediction across the trees in the ensemble. A prediction on a classification problem is the majority vote for the class label across the trees in the ensemble.

Unlike bagging, random forest also involves selecting a subset of input features (columns or variables) at each split point in the construction of trees. Typically, constructing a decision tree involves evaluating the value for each input variable in the data in order to select a split point. By reducing the features to a random subset that may be considered at each split point, it forces each decision tree in the ensemble to be more different.

Random forests provide an improvement over bagged trees by way of a small tweak that decorrelates the trees. [] But when building these decision trees, each time a split in a tree is considered, a random sample of m predictors is chosen as split candidates from the full set of p predictors.

The effect is that the predictions, and in turn, prediction errors, made by each tree in the ensemble are more different or less correlated. When the predictions from these less correlated trees are averaged to make a prediction, it often results in better performance than bagged decision trees.

Random forests tuning parameter is the number of randomly selected predictors, k, to choose from at each split, and is commonly referred to as mtry. In the regression context, Breiman (2001) recommends setting mtry to be one-third of the number of predictors.

Another important hyperparameter to tune is the depth of the decision trees. Deeper trees are often more overfit to the training data, but also less correlated, which in turn may improve the performance of the ensemble. Depths from 1 to 10 levels may be effective.

When using machine learning algorithms that have a stochastic learning algorithm, it is good practice to evaluate them by averaging their performance across multiple runs or repeats of cross-validation. When fitting a final model, it may be desirable to either increase the number of trees until the variance of the model is reduced across repeated evaluations, or to fit multiple final models and average their predictions.

We will evaluate the model using repeated stratified k-fold cross-validation, with three repeats and 10 folds. We will report the mean and standard deviation of the accuracy of the model across all repeats and folds.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

As we did with the last section, we will evaluate the model using repeated k-fold cross-validation, with three repeats and 10 folds. We will report the mean absolute error (MAE) of the model across all repeats and folds. The scikit-learn library makes the MAE negative so that it is maximized instead of minimized. This means that larger negative MAE are better and a perfect model has a MAE of 0.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

A smaller sample size will make trees more different, and a larger sample size will make the trees more similar. Setting max_samples to None will make the sample size the same size as the training dataset and this is the default.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

You might like to extend this example and see what happens if the bootstrap sample size is larger or even much larger than the training dataset (e.g. you can set an integer value as the number of samples instead of a float percentage of the training dataset size).

The example below explores the effect of the number of features randomly selected at each split point on model accuracy. We will try values from 1 to 7 and would expect a small value, around four, to perform well based on the heuristic.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

In this case, the results suggest that a value between three and five would be appropriate, confirming the sensible default of four on this dataset. A value of five might even be better given the smaller standard deviation in classification accuracy as compared to a value of three or four.

Typically, the number of trees is increased until the model performance stabilizes. Intuition might suggest that more trees will lead to overfitting, although this is not the case. Both bagging and random forest algorithms appear to be somewhat immune to overfitting the training dataset given the stochastic nature of the learning algorithm.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

The authors make grand claims about the success of random forests: most accurate, most interpretable, and the like. In our experience random forests do remarkably well, with very little tuning required.

Im implementing a Random Forest and Im getting a shifted time-series in the predictions. If I build the model for predicting e.g. 4 steps ahead, my time-series of predictions seems 4 steps shifted to the right comparing to my time-series of observations. If I try to predict 16 steps ahead, it seems 16 steps shifted.

Very nice tutorial of RF usage! It is really practical to know good practices on those models from my experience Random Forests are very competitive in real industrial applications! (often outperforms such competitors as Artificial Neural Networks). Regards!

Hello Jason, Please I have a question I have the following situation that is already programmed with Logistic regression, I have tried the same program with Random Forest in order to check how it could improve the accuracy. Actually, the accuracy was improved, but I dont know if it is logical to use the Random Forest in my problem case.

My case study is as follow : Based on a market dataset, I need to predict if a customer will buy a product or not depending on his prior history. I.e to know how much a customer bought the same product previously, and how much he just check it without buying it

Id clients CurrectProd P1+ P1- P2+ P2- P3+ P3- PN+ PN- Output 10 CL1 P1, P3 6 1 0 0 8 2 0 0 1 11 CL1 P1, P2 7 1 5 2 0 0 0 0 1

with: CurrentProd: means a list of products that I need to know if a customer will purchase, P1+: mean how many time client buy product 1, P1-: refers to the number that a client checked a product 1 without buying it.

columns present all products existing in the market so that I have data with too many features (min 200 PRODUCT) and at each row the most of those row take value 0 (becose there are not belong to CurrentPRod

Id..|..clients..|..CurrectProd..|.P1+.|.P1-.|.P2+.|.P2-.|.P3+.|.P3-.|. .|.PN+.|.PN-.|.Output 10.|.CL1.|P1, P3|6.|..1|0.|0|..8.|2|. .|0|0.|.1 11.|.CL1.|P1, P2|7.|..1|5.|2|0|0|. .|0|0.|.1

The key will to find an appropriate representation for the problem. This may give you ideas (replace site with product): https://machinelearningmastery.com/faq/single-faq/how-to-develop-forecast-models-for-multiple-sites

Do you know how can I get a graphic representation of the trees in the trained model ? I was trying to use export_graphviz in sklearn, but using cross_val_scores function fitting estimator on its own, i dont know how to use export_gaphviz function.

random forest classifier python code example - data analytics

random forest classifier python code example - data analytics

In this post, you will learn about how to train a Random Forest Classifier using Python Sklearn library. This code will be helpful if you are a beginner data scientist or just want to quickly get code sample to get started with training a machine learning model using Random Forest algorithm. The following topics will be covered:

Random forest can be considered as an ensemble of several decision trees. The idea is to aggregate the prediction outcome of multiple decision trees and create a final outcome based on averaging mechanism (majority voting). It helps the model trained using random forest to generalize better with larger population. In addition, the model becomes less susceptible to overfitting / high variance. Here are the key steps of random forest algorithm:

random forests classifiers in python - datacamp

random forests classifiers in python - datacamp

Random forests is a supervised learning algorithm. It can be used both for classification and regression. It is also the most flexible and easy to use algorithm. A forest is comprised of trees. It is said that the more trees it has, the more robust a forest is. Random forests creates decision trees on randomly selected data samples, gets prediction from each tree and selects the best solution by means of voting. It also provides a pretty good indicator of the feature importance.

Random forests has a variety of applications, such as recommendation engines, image classification and feature selection. It can be used to classify loyal loan applicants, identify fraudulent activity and predict diseases. It lies at the base of the Boruta algorithm, which selects important features in a dataset.

Lets suppose you have decided to ask your friends, and talked with them about their past travel experience to various places. You will get some recommendations from every friend. Now you have to make a list of those recommended places. Then, you ask them to vote (or select one best place for the trip) from the list of recommended places you made. The place with the highest number of votes will be your final choice for the trip.

In the above decision process, there are two parts. First, asking your friends about their individual travel experience and getting one recommendation out of multiple places they have visited. This part is like using the decision tree algorithm. Here, each friend makes a selection of the places he or she has visited so far.

The second part, after collecting all the recommendations, is the voting procedure for selecting the best place in the list of recommendations. This whole process of getting recommendations from friends and voting on them to find the best place is known as the random forests algorithm.

It technically is an ensemble method (based on the divide-and-conquer approach) of decision trees generated on a randomly split dataset. This collection of decision tree classifiers is also known as the forest. The individual decision trees are generated using an attribute selection indicator such as information gain, gain ratio, and Gini index for each attribute. Each tree depends on an independent random sample. In a classification problem, each tree votes and the most popular class is chosen as the final result. In the case of regression, the average of all the tree outputs is considered as the final result. It is simpler and more powerful compared to the other non-linear classification algorithms.

Random forests also offers a good feature selection indicator. Scikit-learn provides an extra variable with the model, which shows the relative importance or contribution of each feature in the prediction. It automatically computes the relevance score of each feature in the training phase. Then it scales the relevance down so that the sum of all scores is 1.

Random forest uses gini importance or mean decrease in impurity (MDI) to calculate the importance of each feature. Gini importance is also known as the total decrease in node impurity. This is how much the model fit or accuracy decreases when you drop a variable. The larger the decrease, the more significant the variable is. Here, the mean decrease is a significant parameter for variable selection. The Gini index can describe the overall explanatory power of the variables.

You will be building a model on the iris flower dataset, which is a very famous classification set. It comprises the sepal length, sepal width, petal length, petal width, and type of flowers. There are three species or classes: setosa, versicolor, and virginia. You will build a model to classify the type of flower. The dataset is available in the scikit-learn library or you can download it from the UCI Machine Learning Repository.

It's a good idea to always explore your data a bit, so you know what you're working with. Here, you can see the first five rows of the dataset are printed, as well as the target variable for the whole dataset.

For visualization, you can use a combination of matplotlib and seaborn. Because seaborn is built on top of matplotlib, it offers a number of customized themes and provides additional plot types. Matplotlib is a superset of seaborn and both are equally important for good visualizations.

You can see that after removing the least important features (sepal length), the accuracy increased. This is because you removed misleading data and noise, resulting in an increased accuracy. A lesser amount of features also reduces the training time.

In this tutorial, you have learned what random forests is, how it works, finding important features, the comparison between random forests and decision trees, advantages and disadvantages. You have also learned model building, evaluation and finding important features in scikit-learn. B

building random forest classifier with python scikit learn

building random forest classifier with python scikit learn

In the Introductory article about random forest algorithm, we addressed how the random forest algorithm works with real life examples. As continues to that, In this article we are going to build the random forest algorithm in python with the help of one of the best Pythonmachine learning library Scikit-Learn.

To build the random forest algorithm we are going to use the Breast Cancer dataset. To summarize in this article we are going to build a random forest classifier to predict the Breast cancer type (Benign or Malignant).

Random forest algorithm is an ensemble classificationalgorithm. Ensemble classifier means a group of classifiers. Instead of using only one classifier to predict the target, In ensemble, we use multiple classifiers to predict the target.

In case, of random forest, these ensemble classifiers are the randomly created decision trees. Each decision tree is a single classifier and the target prediction is based on the majority voting method.

The majority voting conceptis same as the political votings. Each person votes per one political party out all the political parties participating in elections. In the same way, every classifier will votes to one target class out of all the target classes.

To declare the election results. The votes will calculate and the party which got the most number of votes treated as the election winner. In the same way, the target class which got the most number of votes considered as the final predicted target class.

I hope you have a clear understanding of how the random forest algorithm works. Now lets implement the same. As I said earlier, we are going to use the breast cancer dataset to implement the random forest.

Sadly breast cancer is to second most death reason for womens. In the US during the year 2016, almost 246,660 womens breast cancer casesare diagnosed. The myth people believe tumor as cancer but which is not true.

A benign tumor is not a cancerous tumor. Which means its not able to spread through the body like the cancerous tumors. The benign is serious when its growing in sensitive places. This kind of tumors are will well terminated with proper treatment and with the change in diet habits.

If you install the python machine learning packages properly, you wont face any issues. Even though you install the packages properly and you facing the issue ImportError: No module named model_selection. This means the scikit learn package you are using not updated to the new version.

The downloaded dataset is in the data format. So we are going to convert into the CSV format. To do that we are going to write asimple function which first loadsthe data format into the pandas dataframe and later the loaded dataframe will save into the CSV file format.

The loaded dataset doesnt have the header names. So we need to add the header names to the loaded dataframe. To do the same we havewritten a function with takes the dataset and header names as input and add the header names to the dataset.

After adding the header names to dataset we are saving the dataset into CSV format. While saving the file we parameterized the index=False. When we save the loaded dataframe without this the saved file will have an extra column with the indexes. So to eliminate this we are parameterized the index=False.

The best idea to start with is, calculating basic statistics for each column(features and target) of the dataset. You may be wondering what the use of calculating basic statistics of the dataset and how it gonna helps to find the missing values.

Yes, finding the basic statistics will helps us to find the missing values in the dataset. The idea is we can use pandas describe method on the loaded dataset to calculate the basic statistics. This outputs the stats about only the columns which are not having any missing values or categorical values.

As we know the missing values column and the character used to represent the missing values. Now lets write a simple function will take the dataset, header_name and missing value representing the character as input handles the missing values.

Now our dataset is missing values free. Now lets split the data into train and test dataset. The training dataset will use to train the random forest classifier and the test dataset used the validate the model random forest classifier.

To split the data into train and test dataset, Lets write a function which takes the dataset, train percentage, feature header names and target header name as inputs and returns the train_x, test_x, train_y and test_y as outputs.

Where the HEADERS[1:-1]contains all the features header names. We eliminated the first and last header names. The first header name is id and the last header the target header. The HEADERS[-1] contains the target header name.

To train the random forest classifier we are going to use the belowrandom_forest_classifier function. Which requires the features (train_x) and target (train_y) data as inputs and returns the train random forest classifier as output.

First I converted the test_y into list object from pandas dataframe. The reason is as we randomly split the train and test dataset the indexes of the test_y wont be in order. If we convert the dataframe in to list object the indexes will be in order.

When you copy the code in the article, Please check the indentation is properly followed in the code editor you are using, You can compare the code in the article and in your editor. I hope this will resolve issue. If not let me know.

The function in the article which handles the missing values is pretty simple one. From the data itself, we know that having ? represents the missing observation. So in the handling missing values function, we are just checking if the observation is having ? as value then we are not considering those observations.

first, thank you for such a detailed explanation on Machine Learning. But I am facing error while running the random forest example: ### Traceback (most recent call last): File , line 2, in main() File , line 11, in main dataset = handel_missing_values(dataset, HEADERS[6], 7) NameError: global name HEADERS is not defined #### This error is observed when the below code is executed: ### def main(): Main function :return: # Load the csv file into pandas dataframe dataset = pd.read_csv(OUTPUT_PATH) # Get basic statistics of the loaded dataset dataset_statistics(dataset) # Filter missing values dataset = handel_missing_values(dataset, HEADERS[6], 7) ###

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