target encoder sklearn

With a team of extremely dedicated and quality lecturers, target encoder scikit learn will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves.Clear and detailed training . Categorical Feature Encoding Challenge. This Notebook has been released under the Apache 2.0 open source license. sklearn encoders provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. Category Encodersとは？ 公式リファレンスによれば、 A set of scikit-learn-style transformers for encoding categorical variables into numeric with different techniques. Returns: y : numpy array of shape [n_samples] set_params (**params) [source] Set the parameters of this estimator. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. target X = pd. Also, I am not sure that TargetEncoder currently handles polynomial targets correctly. Description. In this tutorial, you will discover how to use encoding schemes for categorical machine learning data. Read more in the User Guide. df = pd.read_csv('data.csv') ct = Column. data , columns = bunch . >>> Target Encoder. Performing data preparation operations, such as scaling, is relatively straightforward for input variables and has been made routine in Python via the Pipeline scikit-learn class. Encode labels with value between 0 and n_classes-1. Target encoding is now available in sklearn through the category_encoders package. A target encoding is any kind of encoding that replaces a feature's categories with some number derived from the target. drop_invariant: bool. 12.1s. This is so that we can match the right encoder in Scikit-learn with the right type of variable. How would Japanese people react to someone refusing to say "itadakimasu" for religious reasons? Notice the categorical text columns have been transformed into numeric columns. An unsupervised example: from category_encoders import * import pandas as pd from sklearn.datasets import load_boston # prepare some data bunch = load_boston y = bunch. Encoding of categorical variables. There are two types of encoders: unsupervised and supervised. class sklearn.preprocessing.OrdinalEncoder (categories='auto', dtype=<class 'numpy.float64'>) [source] Encode categorical features as an integer array. An unsupervised example: from category_encoders import * import pandas as pd from sklearn.datasets import load_boston # prepare some data bunch = load_boston y = bunch. Is swap gate equivalent to just exchanging the wire of the two qubits? Comments (29) Competition Notebook. target_type_str One of: from category_encoders import * import pandas as pd from sklearn.datasets import load_boston # prepare some data bunch = load_boston y = bunch. With a team of extremely dedicated and quality lecturers, sklearn encode will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves.Clear and detailed training methods for each lesson . from sklearn import preprocessing: def target_encoder (df, column, target, index = None, method = 'mean'): """ Target-based encoding is numerization of a categorical variables via the target variable. Encoding of categorical variables¶. Continue exploring. sklearn encode provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. Attributes classes_ndarray of shape (n_classes,) Holds the label for each class. Read more in the User Guide. You can directly search for a documentation of a specific API (e.g. target_encoder_sklearn-transformer Transformer used to pre/post process the target y. feature_encoder_sklearn-transformer Transformer used to pre/post process the features/input X. n_outputs_expected_int The number of outputs the Keras Model is expected to have, as determined by target_transformer_. Logs. Note that when you do target encoding in sklearn, your values may be slightly different than what you get with the above methodology. Target encoding is a fast way to get the most out of your categorical variables with little effort. Logs. See also Target encoder sklearn example. Parameters: alpha (float): smoothing parameter for generalization. Code examples. options are 'error', 'return_nan' and 'value . 11 Categorical Encoders and Benchmark . Class sklearn.preprocessing.LabelEncoder [source] It will encode labels with a value between 0 and -1. With a team of extremely dedicated and quality lecturers, sklearn encoders will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves.Clear and detailed training methods for each lesson . TargetEncoder - The algorithm encodes all features that are submitted to the input based on the target. For each distinct element in x you're going to compute the average of the corresponding values in y. empty row. y, and not the input X. Fit encoder according to X and y. Parameters X array-like, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. arrow_right_alt. In this project we will cover dimensionality reduction using autoencoder methods. This encoding is needed for feeding categorical data to many scikit-learn estimators, notably linear models and SVMs with the standard kernels. target X = pd. boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array). Especially if you're using cross-fold validation you'll want that encoder to be "trained" only on the portion of the training data for each iteration. Comments . history 9 of 9. history Version 2 of 2. Notebook. This reduces the target leakage. Category Encoders Examples. Category encoders. Say you have a categorical variable x and a target y - y can be binary or continuous, it doesn't matter. Ensemble. Encode categorical features as a one-hot numeric array. Returns self. In the example below, we transform the iris.target data. Employee data set. DataFrame ( bunch . Apply Sklearn Label Encoding. In this section, you will see the code example related to how to use LabelEncoder to encode single or multiple columns. This is where you might use sklearn's Pipelines to help. In the output, we can see that the values in the state are encoded with 0,1, and 2. from sklearn. sklearn encoder provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. 7 votes. There are two types of encoders: unsupervised and supervised. Read more in the User Guide. Here we first create an instance of LabelEncoder() and then apply fit_transform by passing the state column of the dataframe. Main purpose is to deal: with high cardinality categorical features without exploding dimensionality. The method works on simple estimators as well as on nested objects (such as pipelines). Counterfeit check writing a function between sets vertically How did the European Union reach the figure of 3% as a maximum allowed deficit? Logs. After completing this tutorial, you will know: Encoding is a required pre-processing step when working with categorical data for machine learning algorithms. BayesianTargetClassifier ( base_estimator=SVC ( kernel="linear" ), encoder=bt. The following are 30 code examples for showing how to use sklearn.preprocessing.LabelEncoder().These examples are extracted from open source projects. Target encoding for categorical features. As we all know that better encoding leads to a better model and most of the algorithms cannot handle the categorical variables unless they are converted into a numerical value. Transform labels back to original encoding. 12.1 second run - successful. eg: In the above example, the number of unique labels is the number of cities. A set of scikit-learn-style transformers for encoding categorical variables into numeric with different techniques. In the example below, we transform the iris.target data. Project: sklearn-onnx Author: onnx File: test_sklearn_ordinal_encoder.py License: MIT License. class sklearn.preprocessing.LabelEncoder. The most common type of encoder is the label encoder, where each unique Label is assigned an integer and we can easily implement it once we know the number of unique labels. from sklearn import datasets from sklearn import preprocessing iris = datasets.load_iris() X = iris.data y = iris.target cat_encoder = preprocessing.OneHotEncoder() encoded = cat_encoder . svm import SVC import bayte as bt ensemble = bt. License. In other languages This page is in other languages . To impute the missing values and encode the target variables using imputer and Label encoder from sklearn in python. handle_missing: str. How to impute missing values and Encode the target variable using sklearn in python? a list of columns to encode, if None, all string columns will be encoded. Linear regression without scikit-learn Exercise M4.01 Solution for Exercise M4.01 Linear regression using scikit-learn Quiz M4.02 Modelling non-linear features-target relationships Exercise M4.02 Solution for Exercise M4.02 Linear regression for a non-linear features-target relationship A Better OrdinalEncoder for Scikit-learn (sklearn) If you ever used Encoder class in Python Sklearn package, you will probably know LabelEncoder, OrdinalEnocder and OneHotEncoder. from sklearn. This is available on a separate Python package called Category Encoders and the algorithm works well along side Scikit-learn, including inside a preprocessing pipeline. The two most popular techniques are an Ordinal Encoding and a One-Hot Encoding. I'm using target encoding on some features in my dataset. Returns self encoder. Output. It is used to target values as y and not the input X. Labelencoder sklearn Example :- LabelEncoder is used to normalize the labels as follows, From sklearn import preprocessing Le=preprocessing.LabelEncoder () Le.fit ( [1, 2, 2, 6]) オンライン学習のコンセプトを取り入れることによりリークを防いでいます(よく理解していない)。 pythonのサンプルです。 Scikit-Learnには該当関数が2021年5月時点でありませんが、category_encodersにはCatBoost Encoderがあります。 While ordinal, one-hot, and hashing encoders have similar equivalents in the existing scikit-learn version, the transformers in this library all share a few useful properties: First-class support for pandas . split (tuple [Union [int, BaseCrossValidator . y array-like, shape = [n_samples] Target values. Target Encoding Target encoding is the process of replacing a categorical value with the mean of the target variable. Given a dataset with two features, we let the encoder find the unique values per feature and transform the data to an ordinal encoding. This transformer should be used to encode target values, i.e.y, and not the input X. A simple and effective version is to apply a group aggregation from Lesson 3, like the mean. An auto-encoder is a kind of unsupervised neural network that is used for dimensionality reduction and feature discovery. Target Encoder¶ class category_encoders.target_encoder.TargetEncoder (verbose=0, cols=None, drop_invariant=False, return_df=True, handle_missing='value', handle_unknown='value', min_samples_leaf=1, smoothing=1.0) [source] ¶. This allows us to encode an arbitrary number of categories without increasing the dimensionality of our data! Example of Ordinal Encoder. Ordered Target Statistics. Data. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. With a team of extremely dedicated and quality lecturers, sklearn encoder will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves.Clear and detailed training methods for each lesson . class sklearn.preprocessing.LabelEncoder [source] ¶ Encode target labels with value between 0 and n_classes-1. Nominal: Unordered Groups. Categorical Data. get_feature_names [source] ¶ Returns the names of all transformed . Category Encoders. Parameters: y : numpy array of shape [n_samples] Target values. With a team of extremely dedicated and quality lecturers, sklearn onehotencoder dataframe will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves.Clear and detailed . If you want to utilize the ensemble methodology described above, construct the same dataset. The "target value" is the y-variable, or the value our model is trying to predict. sklearn.preprocessing.LabelEncoder¶ class sklearn.preprocessing.LabelEncoder [source] ¶. This library comprises a set of transformers which follow the scikit-learn style which means that as well as being used alone, they can also be used inside a scikit-learn pipeline. In this blog, I develope a new Ordinal Encoder which makes up the shortcomings of the current . Note: You can also use target encoding to convert categorical columns to numeric. Data. For example, in leave-one-out encoder the current target value is subtracted from the target statistic. 6.9.1. Autoencoder —. By using Kaggle, you agree to our use of cookies. Input. target encoder scikit learn provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. boolean for whether or not to drop columns with 0 variance. Any non-categorical columns are automatically dropped by the target encoder model. target X = pd. The way it should handle it is described in article A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems in section Extension to Multi-Valued Categorical Targets.In short, for each encoded feature, it should create m-1 columns, where m is the count of . Supported targets: binomial and continuous. This Notebook has been released under the Apache 2.0 open source license. The features are encoded using a one-hot (aka 'one-of-K' or 'dummy') encoding scheme. Why the Scikit-learn library is preferred over the Pandas library when it comes to encoding categorical features; . properties and methods) by adding the @ prefix.. For instance, @append will return the documentation of the append method across all programming languages and libraries. Correctly preparing your training data can mean the difference between mediocre and extraordinary results, even with very simple linear algorithms. Using the Automobiles dataset, this computes the average price of each vehicle's make: This kind of target encoding is sometimes . This replaces the categorical variable This is an example of a regression problem in machine learning as our target variable, test score has a continuous distribution. Data preparation is a big part of applied machine learning. Thus, if the feature is color with values such as ['white', 'red', 'black', 'blue']., using LabelEncoder may encode color string label as [0, 1, 2, 3]. Target (Mean) encoding has a tendency to overfit due to the target leakage. sklearn onehotencoder dataframe provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. feature_names ) # use binary encoding to encode two categorical features enc = BinaryEncoder ( cols = [ 'CHAS' , 'RAD' ]) . import pandas as pd adult_census . There are various techniques to address this problem. This transformer should be used to encode target values, i.e. City and State columns need to be encoded. Currently implemented are: Ordinal One-Hot Binary Helmert Contrast Sum Contrast Polynomial Contrast Backward Difference Contrast Hashing BaseN LeaveOneOut Target . fit ( X . The Sklearn Preprocessing has the module LabelEncoder() that can be used for doing label encoding. Ordinal: Specific ordered Groups. Comments (5) Run. from category_encoders import * import pandas as pd from sklearn.datasets import load_boston # prepare some data bunch = load_boston() y = bunch.target X = pd.DataFrame(bunch.data, columns=bunch.feature_names) # use binary encoding to encode two categorical features enc = BinaryEncoder(cols=['CHAS', 'RAD']).fit(X) # transform the dataset . cat_features = [col for col in X_train.columns if X_train[col].dtype == "object"] # Create the target encoder. Encode target labels with value between 0 and n_classes-1. The idea is quite simple. Category Encoders¶. Examples Given a dataset with two features, we let the encoder find the unique values per feature and transform the data to an ordinal encoding. These are transformers that are not intended to be used on features, only on supervised learning targets. Another technique is to add a Gaussian noise to the encoded value. I am having some problems with using TaregetEncoder inside of sciki's ColumnTransformer. In this post, I want to share this library and give an example of how it can be used in a scikit-learn pipeline. These Encoders are for transforming categorical data to numerical data. Whenever you're using any encoding that uses the target, you'll want to make sure that you're careful about not using leakage. target encoder sklearn example encoder = TargetEncoder()df['Animal Encoded'] = encoder.fit_transform(df['Animal'], df['Target']) Similar pages Similar pages with examples. License. 290.4s . For polynomial target support, see PolynomialWrapper. Cell link copied. class category_encoders.target_encoder.TargetEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, impute_missing=True, handle_unknown='impute', min_samples_leaf=1, smoothing=1) Target Encode for categorical features. Read more in the User Guide. The category-encoder package provides TargetEncoder for target encoding. sklearn.preprocessing .OneHotEncoder ¶. Imputed feature variable Encoded target variable. In [50]: # TODO: create a OneHotEncoder object, and fit it to all of X # 1. If you want to utilize the ensemble methodology described above, construct the same dataset. max_bins (int): maximum number of unique values in a feature. More precisely, an auto-encoder is a feedforward neural network that is trained to predict the input itself. With target encoding, each category is replaced with the mean target value for samples having that category. from sklearn import datasets from sklearn import preprocessing iris = datasets.load_iris() X = iris.data y = iris.target cat_encoder = preprocessing.OneHotEncoder() encoded = cat_encoder . BayesianTargetEncoder ( dist="bernoulli" ) ) Fit the ensemble. Cell link copied. This transformer should be used to encode target values, i.e.y, and not the input X. Transforming the prediction target ( y) ¶. sklearn.preprocessing.OrdinalEncoder. INSTANTIATE enc = preprocessing.OneHotEncoder() # 2. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.preprocessing , or try the search function . To encode categorical variables, we can use the OneHotEncoder class and run fit_transform on the data. This article is in continuation of my previous article that explained how target encoding actually works.The article explained the encoding method on a binary classification task through theory and an example, and how category-encoders library gives incorrect results for multi-class target. This article shows when TargetEncoder of category_encoders fails, gives a snip of the theory behind . scikit-learn 0.24 English . LabelEncoder encodes labels by assigning them numbers. I saw people having simmiliar issues with using Pipelines/ColumnTranformer but have not found any solution. BayesianTargetEncoder ( dist="bernoulli" ) ) Fit the ensemble. Notebook. . Expected Behavior the mean_encoded column must be [10, 30, 30, 30] Actual Behavior the mean_encoded column is [25,30, 25,30] Steps to Reproduce the Problem import pandas as pd from category_encoders import TargetEncoder data={'SubjectNam. A continuous distribution the target encoder sklearn, we transform the iris.target data ( kernel= & quot ; bernoulli & quot bernoulli. May also want to utilize the target encoder sklearn like the mean load the entire dataset... Target labels with value between 0 and n_classes-1 these are transformers that are not intended to be to. Contrast Polynomial Contrast Backward difference Contrast Hashing BaseN LeaveOneOut target as bt ensemble = bt been transformed into numeric.... Can also use target encoding — H2O 3.36.0.1 documentation < /a > sklearn.preprocessing.LabelEncoder. State columns need to be used for dimensionality reduction and feature discovery the search function aggregation Lesson... That we can match the right type of variable estimators as well as nested...: //contrib.scikit-learn.org/category_encoders/catboost.html '' > sklearn.preprocessing.LabelEncoder — scikit-learn... < /a > sklearn.preprocessing.OneHotEncoder.! Such as pipelines ) encoded value > Category Encoders¶ Holds the label for distinct... Bayte as bt ensemble = bt simple estimators as well as on nested objects ( as. Numeric with different techniques target ( y ) — scikit-learn... < /a > ensemble ) ) the... Theory behind H2O 3.36.0.1 documentation < /a > sklearn.preprocessing.OrdinalEncoder: you can also use target encoding done the way! As well as on nested objects ( such as pipelines ) a regression problem in machine learning GeeksforGeeks... Of categories without increasing the dimensionality of our data: //brendanhasz.github.io/2019/03/04/target-encoding '' > TargetEncoder inside ColumnTransformer · #... > How to encode an arbitrary number of cities features without exploding dimensionality ; re going to compute average! Unique values in y corresponding values in a feature construct the same dataset as! In [ 50 ]: # TODO: create a OneHotEncoder object, and Fit it to all X! Target variables using imputer and label encoder from Sklearn in python to say & quot )! How to use encoding schemes for categorical machine learning as our target variable, test score has a distribution.: //www.xpcourse.com/sklearn-encode '' > 6.9: encoding is a feedforward neural network that used... For transforming categorical data with target encoding done the right encoder in scikit-learn with right... Right encoder in scikit-learn with the right type of variable by using Kaggle, you agree to our use cookies. Techniques - machine learning as our target variable, test score has a distribution... Bernoulli & quot ; ) ) Fit the ensemble methodology described above, construct the dataset! People react to someone refusing to say & quot ; linear & quot ; target &. Create a OneHotEncoder object, and 2 df = pd.read_csv ( & # x27 ; data.csv & # x27 s... The corresponding values in y maximum number of categories without target encoder sklearn the dimensionality our! Full pipeline is as such: from sklearn.compose import ColumnTransformer from sklearn.pipeline import pipeline from sklearn.preprocess >... On simple estimators as well as on nested objects ( such as pipelines ): test_sklearn_ordinal_encoder.py license: license. Using Pipelines/ColumnTranformer but have not found any solution swap gate equivalent to just exchanging the wire of the two?! Holds the label for each distinct element in X you & # x27 ; going... To our use of cookies method works on simple estimators as well as on nested (. ; data.csv & # x27 ; re going to compute the average the. ( such as pipelines ) right encoder in scikit-learn with the right type variable! Blog, i develope a new Ordinal encoder which makes up the shortcomings of the target encoder sklearn qubits a snip the... That we can see that the values in a feature of X # 1 are: one-hot... Shortcomings of the theory behind transformed into numeric with different techniques pipelines to help trained to predict > of. Target statistic is in other languages this page is in other languages this page in... Prediction target ( y ) — scikit-learn... < /a > scikit-learn 0.24 English blog i... Trained to predict in y imputer and label encoder from Sklearn in python 2.0 source... Transformers that are submitted to the encoded value > Sklearn encode - encoding of categorical variables¶ Hashing BaseN LeaveOneOut target > Ordered target.... With high cardinality categorical features without exploding dimensionality are automatically dropped by the encoder. Import ColumnTransformer from sklearn.pipeline import pipeline from sklearn.preprocess ) and then apply fit_transform by passing state. > TargetEncoder inside ColumnTransformer · Issue # 206... < /a > class sklearn.preprocessing.LabelEncoder under the Apache 2.0 source... Is where you might use Sklearn & # x27 ; s pipelines to help on! [ 50 ]: # TODO: create a OneHotEncoder object, and not input... Missing values and encode the target statistic been released under the Apache 2.0 open source license use target —. Encoders — Category Encoders s pipelines to help to our use of cookies category-encoders-dev PyPI. The same dataset input based on the target encoder model: # TODO: create a OneHotEncoder object, not!, ) Holds the label for each class even with very simple linear algorithms '' http: ''. Someone refusing to say & quot ; linear & quot ; ) ct =.. Learning targets import pipeline from sklearn.preprocess 0.24 English: onnx File: test_sklearn_ordinal_encoder.py license: MIT license test score a! ; s first load the entire adult dataset containing both numerical and data! To Handle categorical values encoding... < /a > sklearn.preprocessing.OrdinalEncoder encoder the current target value is subtracted from the encoder! The theory behind element in X you & # x27 ; s first load the entire dataset. So that we can match the right encoder in scikit-learn with the right way • Max Halford /a... Japanese people react to someone refusing to say & quot ; ), encoder=bt < href=! ) ) Fit the ensemble methodology described above, construct the same dataset preprocessing.LabelEncoder - scikit-learn - W3cubDocs /a... To apply a group aggregation from Lesson 3, like the mean: numpy of... > scikit-learn 0.24 English the iris.target data to predict that can be used to encode values... ( ) and then apply fit_transform by passing the state are encoded with 0,1, and the. Reduction and feature discovery in other languages ; target value is subtracted from the variables... To be used for dimensionality reduction using autoencoder methods the input X transformed... Use of cookies > example of Ordinal encoder for transforming categorical data with target encoding done the right way Max. Arbitrary number of unique labels is the number of categories without increasing the dimensionality of data! In the output, we will present target encoder sklearn ways of dealing with categorical with. Be encoded ) ct = column TargetEncoder - the algorithm encodes all features that submitted. This page is in other languages > class sklearn.preprocessing.LabelEncoder transforming categorical data target! Will be a numpy array of shape [ n_samples ] target values, i.e.y, and 2: //medium.com/big-data-center-of-excellence/how-to-handle-categorical-values-byaryan-a769b2bb4361 >. Prediction target ( y ) — scikit-learn... < /a > Category.... This allows us to encode an arbitrary number of unique labels is the y-variable, or the value model... By passing the state column of the module sklearn.preprocessing, or try the search function encode categorical into! In other languages this page is in other languages this page is in languages... To predict to compute the average of the DataFrame predict the input X (. 0.24 English: with high cardinality categorical features without exploding dimensionality current target value is subtracted from target! Feature discovery this page is in other languages this page is in languages! Both numerical and categorical data to numerical target encoder sklearn.OneHotEncoder ¶ maximum number of cities schemes for categorical learning! Import bayte as bt ensemble = bt to check out all available functions/classes of the two qubits 2.3.0 <. Shape [ n_samples ] target values, i.e.y, and 2 by the statistic... Value & quot ; ), encoder=bt high cardinality categorical features without exploding dimensionality Encoders and Benchmark tutorial, agree! Drop columns with 0 variance /a > Category Encoders¶ ( base_estimator=SVC ( kernel= & quot ; for religious reasons and! And Benchmark 0.24 English Japanese people react to someone refusing to say & ;. More precisely, an auto-encoder is a feedforward neural network that is trained to predict the itself... Compute the average of the theory behind not the input X > feature encoding techniques machine. We have only taken into account the posterior it to all of X # 1 based on target! Category Encoders¶ = bt completing this tutorial, you will discover How to categorical! Also use target encoding — H2O 3.36.0.1 documentation < /a > sklearn.preprocessing.OneHotEncoder ¶ numerical. High cardinality categorical features without exploding dimensionality a numpy array of shape n_samples. Target variable, test score has a continuous distribution present typical ways of dealing with categorical.! Learning as our target variable, test score has a continuous distribution current. 2.0 open source license, we transform the iris.target data categorical target encoder sklearn to numerical data that can be used encode. Learning algorithms implemented are: Ordinal one-hot Binary Helmert Contrast Sum Contrast Polynomial Contrast difference... The module LabelEncoder ( ) that can be used on features, on! # 206... < /a > example of Ordinal encoder which makes up shortcomings... Project: sklearn-onnx Author: onnx File: test_sklearn_ordinal_encoder.py target encoder sklearn: MIT.... Linear & quot ; itadakimasu & quot ; bernoulli & quot ; ) Fit! When working with categorical data to numerical data use Sklearn & # x27 ; s pipelines to help same... Data can mean the difference between mediocre and extraordinary results, even with very linear...

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