Sklearn Sample

#RandomForests #Classifier #DataScience #ScikitLearn #DigitClassification #DataAnalytics Image Classifiers are used in many places in the industry, In this t. Finally, from sklearn. on novembro 19,. In addition to these built-in toy sample datasets, sklearn. K-1 integer, where K is the number of different classes in the set (in the case of sex, just 0 or 1). Scikit-learn is a python library that is used for machine learning, data processing, cross-validation and more. One of the best things about simple random sampling is the ease of assembling the sample. Package, install, and use your code anywhere. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. A tree structure is constructed that breaks the dataset down into smaller subsets eventually resulting in a prediction. Optimizing hyperparameters for machine learning models is a key step in making accurate predictions. imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. - Upon re-running the experiments, your resulting pipelines may differ (to some extent) from the ones demonstrated here. The default strategy implements one step of the bootstrapping procedure. I recently authored a scikit-learn PR to edit the behavior of train_size and test_size in most of the classes that use it; I thought that their interaction was simple and obvious, but was recently informed otherwise. scikit-learn Machine Learning in Python. Bootstrap If you want each sample to occur at most once you should. naive_bayes module. preprocessing import LabelEncoder le = LabelEncoder() le. train_test_split utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation. Tuning a scikit-learn estimator with skopt. In this post we've discussed the concepts of the Markov property, Markov models and hidden Markov models. 0 is required (update with ‘conda update scikit-learn’)). If you reweight the examples and alpha by the same amount, you'll get the same predictions. Let's start with the sex feature. datasets import load_iris iris = load_iris() data = iris. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class. Kernel-approximations were my first somewhat bigger contribution to scikit-learn and I have been thinking about them for a while. fit function in sklearn how to incorporate the sample weight (time) when building my randomforestregressormodel rf. model_selection we need train_test_split to randomly split data into training and test sets, and GridSearchCV for searching the best parameter for our classifier. cross_validation` module includes utilities for cross-validation and performanc. ttest_ind (a, b, axis=0, equal_var=True, nan_policy='propagate') [source] ¶ Calculate the T-test for the means of two independent samples of scores. By considering different functional neuroimaging applications, the paper illustrates how scikit-learn can be used to perform some key analysis steps. Scikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface. Since the dataset is a simple while it is the most popular dataset frequently used for testing and experimenting with algorithms, we will use it in this tutorial. In this end-to-end Python machine learning tutorial, you'll learn how to use Scikit-Learn to build and tune a supervised learning model! We'll be training and tuning a random forest for wine quality (as judged by wine snobs experts) based on traits like acidity, residual sugar, and alcohol concentration. scikit-learn¶ Scikit is a free and open source machine learning library for Python. In the previous video, we learned how to train three different models and make predictions using those models. It's very common to use a specific train/test split (e. The most common is the R2 score, or coefficient of determination that measures the proportion of the outcomes variation explained by the model, and is the default score function for regression methods in scikit-learn. This documentation is for scikit-learn version. I would start the day and end it with her. Machine learning originated from pattern recognition and computational learning theory in AI. SVM: Weighted samples¶. Classifying Documents with Sklearn’s Count/Hash/TDiF Vectorizers which contains sample. Please refer to the full user guide for further details, as the class and function raw specifications may not be enough to give full guidelines on their uses. Test datasets are small contrived datasets that let you test a machine learning algorithm or test harness. sklearn issue: Found arrays with inconsistent numbers of samples when doing regression 3 How to change the shape of a Pandas Dataframe (row number with an "L")?. The sklearn LR implementation can fit binary, One-vs- Rest, or multinomial logistic regression with optional L2 or L1 regularization. love will be then when my every breath has her name. The example scripts classify iris flower images to build a machine learning model based on scikit-learn's iris dataset. balance_weights¶ sklearn. , a bootstrap sample) from the training set. Why was sample 1 classified as A?. scikit-learn: Accessible and Robust Framework from the Python Ecosystem. Deprecated: Function create_function() is deprecated in /www/wwwroot/autobreeding. 14 and before) of scikit. Start by importing the MissingIndicator from sklearn. The sample weighting rescales the C parameter, which means that the classifier puts more emphasis on getting these points right. Under the hood, this is entirely handled in Python by scikit-learn ‘s IsotonicRegression class. with scikit-learn models in Python. max_sample: int. w2vmodel – Scikit learn wrapper for word2vec model¶. 'n_estimators' indicates the number of trees in the forest. Kaggle competitors spend considerable time on tuning their model in the hopes of winning competitions, and proper model selection plays a huge part in that. See the Multinomial NB docs. You can vote up the examples you like or vote down the ones you don't like. scikit-learn(sklearn)の日本語の入門記事があんまりないなーと思って書きました。 どちらかっていうとよく使う機能の紹介的な感じです。 英語が読める方は公式のチュートリアルがおすすめです。 scikit-learnとは?. Multiclass classification - each sample is assigned to one and only one label Multilabel classification - each sample is assigned a set of target labels - not mutually exclusive, eg preferences. 'n_estimators' indicates the number of trees in the forest. I'm following this example on the scikit-learn website to perform a multioutput classification with a Random Forest model. I'm trying to implement the validation curve based on this SKLearn tutorial. But in this post I am going to use scikit learn to perform linear regression. KNN used in the variety of applications such as finance, healthcare, political science, handwriting detection, image recognition and video recognition. For ranking task, weights are per-group. model_selection we need train_test_split to randomly split data into training and test sets, and GridSearchCV for searching the best parameter for our classifier. preprocessing. Learn how to run your scikit-learn training scripts at enterprise scale using Azure Machine Learning's SKlearn estimator class. scikit-survival – a Python library for survival analysis build on top of scikit-learn | The objective in survival analysis (also referred to as reliability analysis in engineering) is to. MLPRegressor taken from open source projects. 18 and replaced with sklearn. Here are the examples of the python api sklearn. Here our steps are standard scalar and support vector machine. Python For Data Science Cheat Sheet: Scikit-learn. The Silhouette Coefficient is a measure of how well samples are clustered with samples that are similar to themselves. You can vote up the examples you like or vote down the ones you don't like. Here is an example of Hold-out set in practice II: Regression: Remember lasso and ridge regression from the previous chapter? Lasso used the \(L1\) penalty to regularize, while ridge used the \(L2\) penalty. preprocessing import LabelEncoder le = LabelEncoder() le. load_sample_image sklearn. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and. Sci-kit-learn is a popular machine learning package for python and, just like the seaborn package, sklearn comes with some sample datasets ready for you to play with. linear_model. For this, you’ll a dataset which is. Prepare a Scikit-learn Training Script ¶. As a variant you can use stochastic method. A reduced version of the MNIST dataset is one of scikit-learn's included datasets, and that is the one we will use in this exercise. K-means Clustering with Scikit-Learn. It is a nice tool to visualize and understand high-dimensional data. The hidden states can not be observed directly. impute (note that version 0. The summarizing way of addressing this article is to explain how we can implement Decision Tree classifier on Balance scale data set. I would cry for her. This project was a collaboration with SKLearn developers and an attempt to see which parts of Scikit-learn were trivially and usefully parallelizable. It seems that for sklearn. The sklearn LR implementation can fit binary, One-vs- Rest, or multinomial logistic regression with optional L2 or L1 regularization. The data from test datasets have well-defined properties, such as linearly or non-linearity, that allow you to explore specific algorithm behavior. StratifiedShuffleSplit, which can generate subsamples of any size while retaining the structure of the whole dataset, i. on novembro 19,. Scikit-learn is a python library that is used for machine learning, data processing, cross-validation and more. Read the Docs. datasets import make_hastie_10_2 X,y = make_hastie_10_2(n_samples=1000). 5 (2 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. In recent years, it's been a hot topic in both academia and industry, also thanks to the massive popularity of social media which provide a constant source of textual data full of…. Let's also create some extra boolean features that tell us if a sample has a missing value for a certain feature. preprocessing. I recently authored a scikit-learn PR to edit the behavior of train_size and test_size in most of the classes that use it; I thought that their interaction was simple and obvious, but was recently informed otherwise. Each pixel is represented by an integer in the range 0 to 16, indicating varying levels of black. Gemfury is a cloud repository for your private packages. Example using GenSim's LDA and sklearn. The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", aka LFW:. Read more in the User Guide. For a general overview of the Repository, please visit our About page. In this post you will discover how you can install and create your first XGBoost model in Python. Apart from the well-optimized ML routines and pipeline building methods, it also boasts of a solid collection of utility methods for synthetic data generation. fit(labels) # apply encoding to labels labels = le. Problem – Given a dataset of m training examples, each of which contains information in the form of various features and a label. Examples concerning the sklearn. Thierry Bertin-Mahieux, Birchbox, Data Scientist. Hyperparameters define characteristics of the model that can impact model accuracy and computational efficiency. Scikit-learn is our #1 toolkit for all things machine learning at Bestofmedia. It is built on top of Numpy. ” - Arthur Samuel, 1959. The preprocessing module of scikit-learn includes a LabelEncoder class, whose fit method allows conversion of a categorical set into a 0. Python for beginners using sample projects. Now that we know how the K-means clustering algorithm actually works, let's see how we can implement it with Scikit-Learn. StratifiedShuffleSplit, which can generate subsamples of any size while retaining the structure of the whole dataset, i. FeatureHasher are two additional tools that Scikit-Learn includes to support this type of encoding. This is one of the 100+ free recipes of the IPython Cookbook, Second Edition, by Cyrille Rossant, a guide to numerical computing and data science in the Jupyter Notebook. The scikit-learn library offers easy-to-use tools to perform both tokenization and feature extraction of your text data. I would cry for her. Encode categorical integer features using a one-hot aka one-of-K scheme. fit(X) logProb, _ = g. LDA¶ class sklearn. It is built on top of Numpy. This is a demonstration of sentiment analysis using a NLTK 2. Congratulations, you have reached the end of this scikit-learn tutorial, which was meant to introduce you to Python machine learning! Now it's your turn. love will be then when my every breath has her name. The following are code examples for showing how to use sklearn. 0 is required (update with 'conda update scikit-learn')). Bootstrap returns indices of random bootstrap samples from your data. silhouette_samples¶ sklearn. A tree structure is constructed that breaks the dataset down into smaller subsets eventually resulting in a prediction. Although it is a useful tool for building machine learning pipelines, I find it difficult and frustrating to integrate scikit-learn with pandas DataFrames, especially in production code. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class. cross_validation module will no-longer be available in sklearn == 0. 6 compatible source file. Scikit learn consists popular algorithms and. We used the networkx package to create Markov chain diagrams, and sklearn's GaussianMixture to estimate historical regimes. scikit-learn is a high level framework designed for supervised and unsupervised machine learning algorithms. silhouette_samples(). Bootstrap returns indices of random bootstrap samples from your data. Plot decision function of a weighted dataset, where the size of points is proportional to its weight. Everyday low prices and free delivery on eligible orders. from sklearn. Are you a Python programmer looking to get into machine learning? An excellent place to start your journey is by getting acquainted with Scikit-Learn. import matplotlib. scikit-learn example. - microsoft/LightGBM. GitHub Gist: instantly share code, notes, and snippets. "For me the love should start with attraction. In this post we've discussed the concepts of the Markov property, Markov models and hidden Markov models. This Scikit-learn tutorial will help you understand what is Scikit-learn, what can we achieve using Scikit-learn and a demo on how to use Scikit-learn in Pyt. preprocess with MultiLabelBinarizer Multioutput regression - each sample is. Here we are using StandardScaler, which subtracts the mean from each features and then scale to unit variance. The following are code examples for showing how to use sklearn. Decision tree algorithm prerequisites. fit function in sklearn how to incorporate the sample weight (time) when building my randomforestregressormodel rf. Flexible Data Ingestion. make_scorer Make a scorer from a performance metric or loss function. We will program our classifier in Python language and will use its sklearn library. Here we are using StandardScaler, which subtracts the mean from each features and then scale to unit variance. Although it is a useful tool for building machine learning pipelines, I find it difficult and frustrating to integrate scikit-learn with pandas DataFrames, especially in production code. \nit's hard seeing arnold as mr. preprocessing import LabelEncoder le = LabelEncoder() le. A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. OneHotEncoder and sklearn. Import sklearn Note that scikit-learn is imported as sklearn The features of each sample flower are stored in the data attribute of the dataset: >>> print ( iris. 3 Other versions. nSamples is the number of samples in the data. In recent years, it's been a hot topic in both academia and industry, also thanks to the massive popularity of social media which provide a constant source of textual data full of…. In part 2 we will discuss mixture models more in depth. Versions latest stable Downloads On Read the Docs Project Home Builds Free document hosting provided by Read the Docs. Your task is to cluster these objects into two clusters (here you define the value of K (of K-Means) in essence to be 2). After you have installed sklearn and all its dependencies, you are ready to dive further. The Scikit—Learn Function: sklearn. For ranking task, weights are per-group. The main advantage of spark-sklearn is that it enables leveraging the very rich set of machine learning algorithms in scikit-learn. But after training, we have to test the model on some test dataset. Start by importing the MissingIndicator from sklearn. 数学関数を提供。Matlabのような機能を備えている。 SciPy -. We can now create the neural network. Sample pipeline for text feature extraction and evaluation. Finally, from sklearn. The Diabetes dataset has 442 samples with 10 features, making it ideal for getting started with machine learning algorithms. Got the SciPy packages installed? Wondering what to do next? “Scientific Python” doesn’t exist without “Python”. Attributes DEFAULT_VERSION. ensemble import GradientBoostingClassifier. The Scikit—Learn Function: sklearn. #RandomForests #Classifier #DataScience #ScikitLearn #DigitClassification #DataAnalytics Image Classifiers are used in many places in the industry, In this t. I recently authored a scikit-learn PR to edit the behavior of train_size and test_size in most of the classes that use it; I thought that their interaction was simple and obvious, but was recently informed otherwise. Throughout the rest of this article we will see how Python's Scikit-Learn library can be used to implement the random forest algorithm to solve regression, as well as classification, problems. Introduction. The algorithm t-SNE has been merged in the master of scikit learn recently. But after training, we have to test the model on some test dataset. In this article, I'll show you how to use scikit-learn to do machine learning classification on the MNIST database of handwritten digits. with scikit-learn models in Python. scikit-learn Machine Learning in Python. 7 (you need only 30% of diseased eyes from the dataset). grid_search. MICROSOFT PROVIDES AZURE OPEN DATASETS ON AN “AS IS” BASIS. Scikit-learn provides an object-oriented interface centered around the concept of an Estimator. LinearRegression(). Clustering algorithms available in scikit-learn can be used using the PyTools. It can tell you whether it thinks the text you enter below expresses positive sentiment, negative sentiment, or if it's neutral. By voting up you can indicate which examples are most useful and appropriate. Use it if you want to scale. Generalized. They are extracted from open source Python projects. Find file Copy path fmfn Overhaul of examples c6a754e Nov 25, 2018. When you’re working on a model and want to train it, you obviously have a dataset. Covariance estimation. Classifier Building in Scikit-learn. Logistic Regression using Python Video. resample (*arrays, **options) [source] ¶ Resample arrays or sparse matrices in a consistent way. py Find file Copy path CatChenal ENH Weights parameter of datasets. Gilles Louppe, July 2016 Katie Malone, August 2016. My program gives following error: python 1. The library supports state-of-the-art algorithms such as KNN, XGBoost, random forest, SVM among others. In this sklearn with Python for machine learning tutorial, we cover how to do a basic linear SVC example with scikit-learn. The sklearn LR implementation can fit binary, One-vs- Rest, or multinomial logistic regression with optional L2 or L1 regularization. Check the following links for instructions on how to download and install these libraries. The emphasis is on the basics and understanding the resulting. Parameters like C should be tuned to avoid overtraining. def predict (self, X, raw_score = False, num_iteration = None, pred_leaf = False, pred_contrib = False, ** kwargs): """Return the predicted value for each sample. cross_validation. Each row of these matrices corresponds to one sample of the dataset and each column to one variable of the problem. But in this post I am going to use scikit learn to perform linear regression. Just a couple of things you may find yourself doing over and over again when working with scikit-learn. In scikit-learn, we chose a representation of data that is as close as possible to the matrix representation: datasets are encoded as NumPy multidimensional arrays for dense data and as SciPy sparse matrices for sparse data. With scikit learn, you have an entirely different interface and with grid search and vectorizers, you have a lot of options to explore in order to find the optimal model and to present the results. GMM(n_components=1). But we will only use two features from the Iris flower dataset. Scikit-learn (formerly scikits. Basically, a random forests is an ensemble of decision trees. Scikit-learn is a software machine learning library for the Python programming language that has a various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Using pandas with scikit-learn to create Kaggle submissions ¶. auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. scikit-learn is a general-purpose open-source library for data analysis written in python. Kernel-approximations were my first somewhat bigger contribution to scikit-learn and I have been thinking about them for a while. Scikit learn in python plays an integral role in the concept of machine learning and is needed to earn your Python for Data Science Certification. The hdbscan package inherits from sklearn classes, and thus drops in neatly next to other sklearn clusterers with an identical calling API. Decision Trees can be used as classifier or regression models. Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data. Let's also create some extra boolean features that tell us if a sample has a missing value for a certain feature. scikit-learn / scikit-learn. If you use the software, please consider citing scikit-learn. learn and also known as sklearn) is a free software machine learning library for the Python programming language. In random forests (see RandomForestClassifier and RandomForestRegressor classes), each tree in the ensemble is built from a sample drawn with replacement (i. nSamples is the number of samples in the data. - microsoft/LightGBM. Implement scikit-learn into every step of the data science pipeline. This video talks demonstrates the same example on a larger cluster. In simple words, pre-processing refers to the transformations applied to your data before feeding it to the algorithm. Source: scikit-learn Version: 0. In this tutorial we are going to do a simple linear regression using this library, in particular we are going to play with some random generated data that we will use to predict a model. pip install -U scikit-learn pip install -U matplotlib We first import matplotlib. Sample pipeline for text feature extraction and evaluation. In this post you will discover how you can install and create your first XGBoost model in Python. We will be taking a look at some data from the UCI machine learning repository. If float, should be between 0. Scikit-learn (Pedregosa et al. pyplot as plt import seaborn as sns import pandas as pd import numpy as np %matplotlib inline We will simulate data using scikit-learn’s make-blobs module in sklearn. You can vote up the examples you like or vote down the ones you don't like. 5 (2 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. I have a class imbalance problem and been experimenting with a weighted Random Forest using the implementation in scikit-learn (>= 0. scikit-learn example. If you use the software, please consider citing scikit-learn. values) Your answer should be 0. Scikit-learn is an open source Python library for machine learning. naive_bayes module. preprocessing. Tuning a scikit-learn estimator with skopt. Posted 2 years ago by [email protected] Welcome back to my video series on machine learning in Python with scikit-learn. Here our steps are standard scalar and support vector machine. Is it correct? If yes, how does the sample_weight work?. "For me the love should start with attraction. To evaluate the impact of the scale of the dataset (n_samples and n_features) while controlling the statistical properties of the data (typically the correlation and informativeness of the features), it is also possible to generate synthetic data. Learn Cheatsheet: Reference and Examples single sample. Training random forest classifier with scikit learn. predicting customer churn with scikit learn and yhat by eric chiang Customer Churn "Churn Rate" is a business term describing the rate at which customers leave or cease paying for a product or service. , a bootstrap sample) from the training set. Each sample in this scikit-learn dataset is an 8x8 image representing a handwritten digit. They are extracted from open source Python projects. For various reasons, I need to assume that the frequency of occurrence per unit of time is constant for any length of time period so I want to use the time as a sample weight. The non-core sample is assigned to whichever cluster is generated first in a pass through the data, and so the results will depend on the data ordering. Birch (threshold=0. 4 powered text classification process. 6705165630156111. Scikit-learn is a free machine learning library for Python. datasets import load_iris iris = load_iris() data = iris. load_svmlight_file for the svmlight or libSVM sparse format; scikit-learn's datasets. Clustering allows us to better understand how a sample might be comprised of distinct subgroups given a set of variables. OneHotEncoder and sklearn. Versions latest stable Downloads On Read the Docs Project Home Builds Free document hosting provided by Read the Docs. datasets import load_boston >>> from sklearn Returns the index of the leaf that each sample is predicted. Now we are ready to create a pipeline object by providing with the list of steps. Getting our data. When using scikit-learn's grid_search API, legal tunable parameters are those you could pass to sk_params, including fitting parameters. datasets import make_hastie_10_2 X,y = make_hastie_10_2(n_samples=1000). The most applicable machine learning algorithm for our problem is Linear SVC. pyplot as plt import seaborn as sns import pandas as pd import numpy as np %matplotlib inline We will simulate data using scikit-learn’s make-blobs module in sklearn. In simple words, pre-processing refers to the transformations applied to your data before feeding it to the algorithm. Supervised Learning for Document Classification with Scikit-Learn By QuantStart Team This is the first article in what will become a set of tutorials on how to carry out natural language document classification, for the purposes of sentiment analysis and, ultimately, automated trade filter or signal generation. metrics module). SVC, different scaling of sample_weight makes the classifier behaves differently. Prepare a Scikit-learn Training Script ¶. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and. It features various algorithms like support vector machine, random forests, and k-neighbours, and it also supports Python numerical and scientific libraries like NumPy and SciPy. Chapter No. preprocessing. A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule. transform(labels) The table below shows labels before and after the data transformation, and was created using df. The scikit-learn Python library provides a. 数学関数を提供。Matlabのような機能を備えている。 SciPy -. In this post you will get an overview of the scikit-learn library and useful references of. We used the networkx package to create Markov chain diagrams, and sklearn's GaussianMixture to estimate historical regimes. LinearRegression(). Original description is available here and the original data file is avilable here. A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition [Sebastian Raschka, Vahid Mirjalili] on Amazon. i should feel that I need her every time around me. They are extracted from open source Python projects. SMOTE uses a nearest neighbors algorithm to generate new and synthetic data we can use for training our model. GridSearchCV(). *FREE* shipping on qualifying offers. fit function in sklearn how to incorporate the sample weight (time) when building my randomforestregressormodel rf. The fraction of samples to be used in each randomized design. Learn Cheatsheet: Reference and Examples single sample. LDA¶ class sklearn. It's simple, reliable, and hassle-free. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: