1641636000
Lazy Prediction Helps to Build Many Underlying Models
Lazy Predict
Lazy Predict helps build a lot of basic models without much code and helps understand which models works better without any parameter tuning.
- Free software: MIT license
- Documentation: https://lazypredict.readthedocs.io.
Installation
To install Lazy Predict:
pip install lazypredict
Usage
To use Lazy Predict in a project:
import lazypredict
Classification
Example
from lazypredict.Supervised import LazyClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
data = load_breast_cancer()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =123)
clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)
models,predictions = clf.fit(X_train, X_test, y_train, y_test)
print(models)
| Model | Accuracy | Balanced Accuracy | ROC AUC | F1 Score | Time Taken |
|:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:|
| LinearSVC | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0150008 |
| SGDClassifier | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0109992 |
| MLPClassifier | 0.985965 | 0.986904 | 0.986904 | 0.985994 | 0.426 |
| Perceptron | 0.985965 | 0.984797 | 0.984797 | 0.985965 | 0.0120046 |
| LogisticRegression | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.0200036 |
| LogisticRegressionCV | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.262997 |
| SVC | 0.982456 | 0.979942 | 0.979942 | 0.982437 | 0.0140011 |
| CalibratedClassifierCV | 0.982456 | 0.975728 | 0.975728 | 0.982357 | 0.0350015 |
| PassiveAggressiveClassifier | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0130005 |
| LabelPropagation | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0429988 |
| LabelSpreading | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0310006 |
| RandomForestClassifier | 0.97193 | 0.969594 | 0.969594 | 0.97193 | 0.033 |
| GradientBoostingClassifier | 0.97193 | 0.967486 | 0.967486 | 0.971869 | 0.166998 |
| QuadraticDiscriminantAnalysis | 0.964912 | 0.966206 | 0.966206 | 0.965052 | 0.0119994 |
| HistGradientBoostingClassifier | 0.968421 | 0.964739 | 0.964739 | 0.968387 | 0.682003 |
| RidgeClassifierCV | 0.97193 | 0.963272 | 0.963272 | 0.971736 | 0.0130029 |
| RidgeClassifier | 0.968421 | 0.960525 | 0.960525 | 0.968242 | 0.0119977 |
| AdaBoostClassifier | 0.961404 | 0.959245 | 0.959245 | 0.961444 | 0.204998 |
| ExtraTreesClassifier | 0.961404 | 0.957138 | 0.957138 | 0.961362 | 0.0270066 |
| KNeighborsClassifier | 0.961404 | 0.95503 | 0.95503 | 0.961276 | 0.0560005 |
| BaggingClassifier | 0.947368 | 0.954577 | 0.954577 | 0.947882 | 0.0559971 |
| BernoulliNB | 0.950877 | 0.951003 | 0.951003 | 0.951072 | 0.0169988 |
| LinearDiscriminantAnalysis | 0.961404 | 0.950816 | 0.950816 | 0.961089 | 0.0199995 |
| GaussianNB | 0.954386 | 0.949536 | 0.949536 | 0.954337 | 0.0139935 |
| NuSVC | 0.954386 | 0.943215 | 0.943215 | 0.954014 | 0.019989 |
| DecisionTreeClassifier | 0.936842 | 0.933693 | 0.933693 | 0.936971 | 0.0170023 |
| NearestCentroid | 0.947368 | 0.933506 | 0.933506 | 0.946801 | 0.0160074 |
| ExtraTreeClassifier | 0.922807 | 0.912168 | 0.912168 | 0.922462 | 0.0109999 |
| CheckingClassifier | 0.361404 | 0.5 | 0.5 | 0.191879 | 0.0170043 |
| DummyClassifier | 0.512281 | 0.489598 | 0.489598 | 0.518924 | 0.0119965 |
Regression
Example
from lazypredict.Supervised import LazyRegressor
from sklearn import datasets
from sklearn.utils import shuffle
import numpy as np
boston = datasets.load_boston()
X, y = shuffle(boston.data, boston.target, random_state=13)
X = X.astype(np.float32)
offset = int(X.shape[0] * 0.9)
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]
reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)
print(models)
| Model | Adjusted R-Squared | R-Squared | RMSE | Time Taken |
|:------------------------------|-------------------:|----------:|------:|-----------:|
| SVR | 0.83 | 0.88 | 2.62 | 0.01 |
| BaggingRegressor | 0.83 | 0.88 | 2.63 | 0.03 |
| NuSVR | 0.82 | 0.86 | 2.76 | 0.03 |
| RandomForestRegressor | 0.81 | 0.86 | 2.78 | 0.21 |
| XGBRegressor | 0.81 | 0.86 | 2.79 | 0.06 |
| GradientBoostingRegressor | 0.81 | 0.86 | 2.84 | 0.11 |
| ExtraTreesRegressor | 0.79 | 0.84 | 2.98 | 0.12 |
| AdaBoostRegressor | 0.78 | 0.83 | 3.04 | 0.07 |
| HistGradientBoostingRegressor | 0.77 | 0.83 | 3.06 | 0.17 |
| PoissonRegressor | 0.77 | 0.83 | 3.11 | 0.01 |
| LGBMRegressor | 0.77 | 0.83 | 3.11 | 0.07 |
| KNeighborsRegressor | 0.77 | 0.83 | 3.12 | 0.01 |
| DecisionTreeRegressor | 0.65 | 0.74 | 3.79 | 0.01 |
| MLPRegressor | 0.65 | 0.74 | 3.80 | 1.63 |
| HuberRegressor | 0.64 | 0.74 | 3.84 | 0.01 |
| GammaRegressor | 0.64 | 0.73 | 3.88 | 0.01 |
| LinearSVR | 0.62 | 0.72 | 3.96 | 0.01 |
| RidgeCV | 0.62 | 0.72 | 3.97 | 0.01 |
| BayesianRidge | 0.62 | 0.72 | 3.97 | 0.01 |
| Ridge | 0.62 | 0.72 | 3.97 | 0.01 |
| TransformedTargetRegressor | 0.62 | 0.72 | 3.97 | 0.01 |
| LinearRegression | 0.62 | 0.72 | 3.97 | 0.01 |
| ElasticNetCV | 0.62 | 0.72 | 3.98 | 0.04 |
| LassoCV | 0.62 | 0.72 | 3.98 | 0.06 |
| LassoLarsIC | 0.62 | 0.72 | 3.98 | 0.01 |
| LassoLarsCV | 0.62 | 0.72 | 3.98 | 0.02 |
| Lars | 0.61 | 0.72 | 3.99 | 0.01 |
| LarsCV | 0.61 | 0.71 | 4.02 | 0.04 |
| SGDRegressor | 0.60 | 0.70 | 4.07 | 0.01 |
| TweedieRegressor | 0.59 | 0.70 | 4.12 | 0.01 |
| GeneralizedLinearRegressor | 0.59 | 0.70 | 4.12 | 0.01 |
| ElasticNet | 0.58 | 0.69 | 4.16 | 0.01 |
| Lasso | 0.54 | 0.66 | 4.35 | 0.02 |
| RANSACRegressor | 0.53 | 0.65 | 4.41 | 0.04 |
| OrthogonalMatchingPursuitCV | 0.45 | 0.59 | 4.78 | 0.02 |
| PassiveAggressiveRegressor | 0.37 | 0.54 | 5.09 | 0.01 |
| GaussianProcessRegressor | 0.23 | 0.43 | 5.65 | 0.03 |
| OrthogonalMatchingPursuit | 0.16 | 0.38 | 5.89 | 0.01 |
| ExtraTreeRegressor | 0.08 | 0.32 | 6.17 | 0.01 |
| DummyRegressor | -0.38 | -0.02 | 7.56 | 0.01 |
| LassoLars | -0.38 | -0.02 | 7.56 | 0.01 |
| KernelRidge | -11.50 | -8.25 | 22.74 | 0.01 |
Warning
Regression and Classification are replaced with LazyRegressor and LazyClassifier. Regression and Classification classes will be removed in next release
Author: shankarpandala
Source Code: https://github.com/shankarpandala/lazypredict
License: MIT License
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1623223443
Predictive Modeling in Data Science
Predictive modeling is an integral tool used in the data science world — learn the five primary predictive models and how to use them properly.
Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.
Top 5 Predictive Models
- Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer “yes or no” types of questions.
- Clustering Model: This model groups data points into separate groups, based on similar behavior.
- **Forecast Model: **One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
- Outliers Model: This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
- Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.
#big data #data science #predictive analytics #predictive analysis #predictive modeling #predictive models
1607523900
How To Create Tabs in Less Than 12 Minutes Using HTML CSS
In this video, We have created a Tab design in HTML and CSS without using JavaScript. I have also provided HTML and CSS code on my website, you can visit my website by clicking given link.
Subscribe: https://www.youtube.com/@CodingLabYT/featured
Source Code :
HTML :
<!DOCTYPE html>
<html lang="en" dir="ltr">
<head>
<meta charset="UTF-8">
<!--<title> CSS Vertical Tabs </title>-->
<link rel="stylesheet" href="style.css">
<!-- Fontawesome CDN Link -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.2/css/all.min.css"/>
<meta name="viewport" content="width=device-width, initial-scale=1.0">
</head>
<body>
<div class="container">
<div class="topic">CSS Vertical Tabs.</div>
<div class="content">
<input type="radio" name="slider" checked id="home">
<input type="radio" name="slider" id="blog">
<input type="radio" name="slider" id="help">
<input type="radio" name="slider" id="code">
<input type="radio" name="slider" id="about">
<div class="list">
<label for="home" class="home">
<i class="fas fa-home"></i>
<span class="title">Home</span>
</label>
<label for="blog" class="blog">
<span class="icon"><i class="fas fa-blog"></i></span>
<span class="title">Blog</span>
</label>
<label for="help" class="help">
<span class="icon"><i class="far fa-envelope"></i></span>
<span class="title">Help</span>
</label>
<label for="code" class="code">
<span class="icon"><i class="fas fa-code"></i></span>
<span class="title">Code</span>
</label>
<label for="about" class="about">
<span class="icon"><i class="far fa-user"></i></span>
<span class="title">About</span>
</label>
<div class="slider"></div>
</div>
<div class="text-content">
<div class="home text">
<div class="title">Home Content</div>
<p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Quasi excepturi ducimus sequi dignissimos expedita tempore omnis quos cum, possimus, aspernatur esse nihil commodi est maiores dolorum rem iusto atque, beatae voluptas sit eligendi architecto dolorem temporibus. Non magnam ipsam, voluptas quasi nam dicta ut. Ad corrupti aliquid obcaecati alias, nemo veritatis porro nisi eius sequi dignissimos ea repellendus quibusdam minima ipsum animi quae, libero quisquam a! Laudantium iste est sapiente, ullam itaque odio iure laborum voluptatem quaerat tempore doloremque quam modi, atque minima enim saepe! Dolorem rerum minima incidunt, officia!</p>
</div>
<div class="blog text">
<div class="title">Blog Content</div>
<p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Alias tempora, unde reprehenderit incidunt excepturi blanditiis ullam dignissimos provident quam? Fugit, enim! Architecto ad officiis dignissimos ex quae iusto amet pariatur, ea eius aut velit, tempora magnam hic autem maiores unde corrupti tenetur delectus! Voluptatum praesentium labore consectetur ea qui illum illo distinctio, sunt, ipsam rerum optio quibusdam cum a? Aut facilis non fuga molestiae voluptatem omnis reprehenderit, dignissimos commodi repellat sapiente natus ipsam, ipsa distinctio. Ducimus repudiandae fuga aliquid, numquam.</p>
</div>
<div class="help text">
<div class="title">Help Content</div>
<p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Maiores error neque, officia excepturi dolores quis dolor, architecto iusto deleniti a soluta nostrum. Fuga reiciendis beatae, dicta voluptatem, vitae eligendi maxime accusamus. Amet totam aut odio velit cumque autem neque sequi provident mollitia, nisi sunt maiores facilis debitis in officiis asperiores saepe quo soluta laudantium ad non quisquam! Repellendus culpa necessitatibus aliquam quod mollitia perspiciatis ducimus doloribus perferendis autem, omnis, impedit, veniam qui dolorem? Ipsam nihil assumenda, sit ratione blanditiis eius aliquam libero iusto, dolorum aut perferendis modi laboriosam sint dolor.</p>
</div>
<div class="code text">
<div class="title">Code Content</div>
<p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Tempore magnam vitae inventore blanditiis nam tenetur voluptates doloribus error atque reprehenderit, necessitatibus minima incidunt a eius corrupti placeat, quasi similique deserunt, harum? Quia ut impedit ab earum expedita soluta repellat perferendis hic tempora inventore, accusantium porro consequuntur quisquam et assumenda distinctio dignissimos doloremque enim nemo delectus deserunt! Ullam perspiciatis quae aliquid animi quam amet deleniti, at dolorum tenetur, tempore laborum.</p>
</div>
<div class="about text">
<div class="title">About Content</div>
<p>Lorem ipsum dolor sit amet, consectetur adipisicing elit. Necessitatibus incidunt possimus quas ad, sit nam veniam illo ullam sapiente, aspernatur fugiat atque. Laboriosam libero voluptatum molestiae veniam earum quisquam, laudantium aperiam, eligendi dicta animi maxime sunt non nisi, ex, ipsa! Soluta ex, quibusdam voluptatem distinctio asperiores recusandae veritatis optio dolorem illo nesciunt quos ullam, dicta numquam ipsam cumque sed. Blanditiis omnis placeat, enim sit dicta eligendi voluptatibus laborum consectetur repudiandae tempora numquam molestiae rerum mollitia nemo. Velit perspiciatis, nesciunt, quo illo quas error debitis molestiae et sapiente neque tempore natus?</p>
</div>
</div>
</div>
</div>
</body>
</html>
CSS :
@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@200;300;400;500;600;700&display=swap');
*{
margin: 0;
padding: 0;
box-sizing: border-box;
font-family: 'Poppins', sans-serif;
}
body{
height: 100vh;
display: flex;
align-items: center;
justify-content: center;
background: #dad3f8;
}
::selection{
background: #6d50e2;
color: #fff;
}
.container{
max-width: 950px;
width: 100%;
padding: 40px 50px 40px 40px;
background: #fff;
margin: 0 20px;
border-radius: 12px;
box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.container .topic{
font-size: 30px;
font-weight: 500;
margin-bottom: 20px;
}
.content{
display: flex;
align-items: center;
justify-content: space-between;
}
.content .list{
display: flex;
flex-direction: column;
width: 20%;
margin-right: 50px;
position: relative;
}
.content .list label{
height: 60px;
font-size: 22px;
font-weight: 500;
line-height: 60px;
cursor: pointer;
padding-left: 25px;
transition: all 0.5s ease;
color: #333;
z-index: 12;
}
#home:checked ~ .list label.home,
#blog:checked ~ .list label.blog,
#help:checked ~ .list label.help,
#code:checked ~ .list label.code,
#about:checked ~ .list label.about{
color: #fff;
}
.content .list label:hover{
color: #6d50e2;
}
.content .slider{
position: absolute;
left: 0;
top: 0;
height: 60px;
width: 100%;
border-radius: 12px;
background: #6d50e2;
transition: all 0.4s ease;
}
#home:checked ~ .list .slider{
top: 0;
}
#blog:checked ~ .list .slider{
top: 60px;
}
#help:checked ~ .list .slider{
top: 120px;
}
#code:checked ~ .list .slider{
top: 180px;
}
#about:checked ~ .list .slider{
top: 240px;
}
.content .text-content{
width: 80%;
height: 100%;
}
.content .text{
display: none;
}
.content .text .title{
font-size: 25px;
margin-bottom: 10px;
font-weight: 500;
}
.content .text p{
text-align: justify;
}
.content .text-content .home{
display: block;
}
#home:checked ~ .text-content .home,
#blog:checked ~ .text-content .blog,
#help:checked ~ .text-content .help,
#code:checked ~ .text-content .code,
#about:checked ~ .text-content .about{
display: block;
}
#blog:checked ~ .text-content .home,
#help:checked ~ .text-content .home,
#code:checked ~ .text-content .home,
#about:checked ~ .text-content .home{
display: none;
}
.content input{
display: none;
}#javascript #html #css
1596170547
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1641636000
Lazy Prediction Helps to Build Many Underlying Models
Lazy Predict
Lazy Predict helps build a lot of basic models without much code and helps understand which models works better without any parameter tuning.
- Free software: MIT license
- Documentation: https://lazypredict.readthedocs.io.
Installation
To install Lazy Predict:
pip install lazypredict
Usage
To use Lazy Predict in a project:
import lazypredict
Classification
Example
from lazypredict.Supervised import LazyClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
data = load_breast_cancer()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =123)
clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)
models,predictions = clf.fit(X_train, X_test, y_train, y_test)
print(models)
| Model | Accuracy | Balanced Accuracy | ROC AUC | F1 Score | Time Taken |
|:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:|
| LinearSVC | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0150008 |
| SGDClassifier | 0.989474 | 0.987544 | 0.987544 | 0.989462 | 0.0109992 |
| MLPClassifier | 0.985965 | 0.986904 | 0.986904 | 0.985994 | 0.426 |
| Perceptron | 0.985965 | 0.984797 | 0.984797 | 0.985965 | 0.0120046 |
| LogisticRegression | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.0200036 |
| LogisticRegressionCV | 0.985965 | 0.98269 | 0.98269 | 0.985934 | 0.262997 |
| SVC | 0.982456 | 0.979942 | 0.979942 | 0.982437 | 0.0140011 |
| CalibratedClassifierCV | 0.982456 | 0.975728 | 0.975728 | 0.982357 | 0.0350015 |
| PassiveAggressiveClassifier | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0130005 |
| LabelPropagation | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0429988 |
| LabelSpreading | 0.975439 | 0.974448 | 0.974448 | 0.975464 | 0.0310006 |
| RandomForestClassifier | 0.97193 | 0.969594 | 0.969594 | 0.97193 | 0.033 |
| GradientBoostingClassifier | 0.97193 | 0.967486 | 0.967486 | 0.971869 | 0.166998 |
| QuadraticDiscriminantAnalysis | 0.964912 | 0.966206 | 0.966206 | 0.965052 | 0.0119994 |
| HistGradientBoostingClassifier | 0.968421 | 0.964739 | 0.964739 | 0.968387 | 0.682003 |
| RidgeClassifierCV | 0.97193 | 0.963272 | 0.963272 | 0.971736 | 0.0130029 |
| RidgeClassifier | 0.968421 | 0.960525 | 0.960525 | 0.968242 | 0.0119977 |
| AdaBoostClassifier | 0.961404 | 0.959245 | 0.959245 | 0.961444 | 0.204998 |
| ExtraTreesClassifier | 0.961404 | 0.957138 | 0.957138 | 0.961362 | 0.0270066 |
| KNeighborsClassifier | 0.961404 | 0.95503 | 0.95503 | 0.961276 | 0.0560005 |
| BaggingClassifier | 0.947368 | 0.954577 | 0.954577 | 0.947882 | 0.0559971 |
| BernoulliNB | 0.950877 | 0.951003 | 0.951003 | 0.951072 | 0.0169988 |
| LinearDiscriminantAnalysis | 0.961404 | 0.950816 | 0.950816 | 0.961089 | 0.0199995 |
| GaussianNB | 0.954386 | 0.949536 | 0.949536 | 0.954337 | 0.0139935 |
| NuSVC | 0.954386 | 0.943215 | 0.943215 | 0.954014 | 0.019989 |
| DecisionTreeClassifier | 0.936842 | 0.933693 | 0.933693 | 0.936971 | 0.0170023 |
| NearestCentroid | 0.947368 | 0.933506 | 0.933506 | 0.946801 | 0.0160074 |
| ExtraTreeClassifier | 0.922807 | 0.912168 | 0.912168 | 0.922462 | 0.0109999 |
| CheckingClassifier | 0.361404 | 0.5 | 0.5 | 0.191879 | 0.0170043 |
| DummyClassifier | 0.512281 | 0.489598 | 0.489598 | 0.518924 | 0.0119965 |
Regression
Example
from lazypredict.Supervised import LazyRegressor
from sklearn import datasets
from sklearn.utils import shuffle
import numpy as np
boston = datasets.load_boston()
X, y = shuffle(boston.data, boston.target, random_state=13)
X = X.astype(np.float32)
offset = int(X.shape[0] * 0.9)
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]
reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)
print(models)
| Model | Adjusted R-Squared | R-Squared | RMSE | Time Taken |
|:------------------------------|-------------------:|----------:|------:|-----------:|
| SVR | 0.83 | 0.88 | 2.62 | 0.01 |
| BaggingRegressor | 0.83 | 0.88 | 2.63 | 0.03 |
| NuSVR | 0.82 | 0.86 | 2.76 | 0.03 |
| RandomForestRegressor | 0.81 | 0.86 | 2.78 | 0.21 |
| XGBRegressor | 0.81 | 0.86 | 2.79 | 0.06 |
| GradientBoostingRegressor | 0.81 | 0.86 | 2.84 | 0.11 |
| ExtraTreesRegressor | 0.79 | 0.84 | 2.98 | 0.12 |
| AdaBoostRegressor | 0.78 | 0.83 | 3.04 | 0.07 |
| HistGradientBoostingRegressor | 0.77 | 0.83 | 3.06 | 0.17 |
| PoissonRegressor | 0.77 | 0.83 | 3.11 | 0.01 |
| LGBMRegressor | 0.77 | 0.83 | 3.11 | 0.07 |
| KNeighborsRegressor | 0.77 | 0.83 | 3.12 | 0.01 |
| DecisionTreeRegressor | 0.65 | 0.74 | 3.79 | 0.01 |
| MLPRegressor | 0.65 | 0.74 | 3.80 | 1.63 |
| HuberRegressor | 0.64 | 0.74 | 3.84 | 0.01 |
| GammaRegressor | 0.64 | 0.73 | 3.88 | 0.01 |
| LinearSVR | 0.62 | 0.72 | 3.96 | 0.01 |
| RidgeCV | 0.62 | 0.72 | 3.97 | 0.01 |
| BayesianRidge | 0.62 | 0.72 | 3.97 | 0.01 |
| Ridge | 0.62 | 0.72 | 3.97 | 0.01 |
| TransformedTargetRegressor | 0.62 | 0.72 | 3.97 | 0.01 |
| LinearRegression | 0.62 | 0.72 | 3.97 | 0.01 |
| ElasticNetCV | 0.62 | 0.72 | 3.98 | 0.04 |
| LassoCV | 0.62 | 0.72 | 3.98 | 0.06 |
| LassoLarsIC | 0.62 | 0.72 | 3.98 | 0.01 |
| LassoLarsCV | 0.62 | 0.72 | 3.98 | 0.02 |
| Lars | 0.61 | 0.72 | 3.99 | 0.01 |
| LarsCV | 0.61 | 0.71 | 4.02 | 0.04 |
| SGDRegressor | 0.60 | 0.70 | 4.07 | 0.01 |
| TweedieRegressor | 0.59 | 0.70 | 4.12 | 0.01 |
| GeneralizedLinearRegressor | 0.59 | 0.70 | 4.12 | 0.01 |
| ElasticNet | 0.58 | 0.69 | 4.16 | 0.01 |
| Lasso | 0.54 | 0.66 | 4.35 | 0.02 |
| RANSACRegressor | 0.53 | 0.65 | 4.41 | 0.04 |
| OrthogonalMatchingPursuitCV | 0.45 | 0.59 | 4.78 | 0.02 |
| PassiveAggressiveRegressor | 0.37 | 0.54 | 5.09 | 0.01 |
| GaussianProcessRegressor | 0.23 | 0.43 | 5.65 | 0.03 |
| OrthogonalMatchingPursuit | 0.16 | 0.38 | 5.89 | 0.01 |
| ExtraTreeRegressor | 0.08 | 0.32 | 6.17 | 0.01 |
| DummyRegressor | -0.38 | -0.02 | 7.56 | 0.01 |
| LassoLars | -0.38 | -0.02 | 7.56 | 0.01 |
| KernelRidge | -11.50 | -8.25 | 22.74 | 0.01 |
Warning
Regression and Classification are replaced with LazyRegressor and LazyClassifier. Regression and Classification classes will be removed in next release
Author: shankarpandala
Source Code: https://github.com/shankarpandala/lazypredict
License: MIT License
1622449192
The Implication Of Porter's Five Forces Model For The Business Update
The porter five forces model contains
:
1. Supplier Energy. An evaluation of the way for suppliers to pressure up prices. This is driven using the: quantity of suppliers of every important input; strong point in their service or product; relative size and energy of the supplier; and price of switching from one dealer to any other.
2. Customer Power. An evaluation of the way cleans it’s far for buyers to drive charges down. This is pushed through the number of consumers in the market; the importance of each person client to the organization; and the price to the client of switching from one provider to any other. If a commercial enterprise has just a few effective customers, they’re regularly capable of dictate phrases.
3.Competitive Competition. The main driving force is the range and functionality of competition inside the marketplace. Many competitors, offering undifferentiated services and products, will lessen marketplace beauty.
4. Chance of substitution. In which near alternative merchandise exists in a marketplace, it will increase the chance of clients switching to alternatives in response to price will increase. This reduces the power of suppliers and the attractiveness of the marketplace.
5. Hazard of recent entry. Profitable markets attract new entrants, which erodes profitability. Until incumbents have robust and sturdy boundaries to access, for example, patents, economies of scale, capital requirements, or authorities’ policies, then profitability will decline to an aggressive price.
Porter’s five forces decide a corporation’s competitive surroundings, which influence profitability. The bargaining strength of customers and suppliers affects a small agency’s capability to increase costs and control costs, respectively. However, if there’s the simplest one provider for a selected element, then that supplier has bargaining power over its customers. Low-entry boundaries appeal to new opposition, while excessive-access barriers discourage it. Enterprise contention is possibly to be higher while several companies are competing for equal clients, and the extreme competition ends in lower charges and earnings.
The five competitive forces monitor that opposition extends past present-day competitors. Customers, suppliers, substitutes, and capability entrants—together referred to as a prolonged contention—are competition to agencies inside an enterprise.
Porter’s five forces model mutually determine the strength of enterprise opposition and profitability. The strongest force guidelines and must be the focus of any enterprise analysis and ensuing aggressive strategy.
Brief-term elements that affect competition and profitability must be prominent from the competitive forces that shape the underlying structure of an enterprise. Expert Assignment help explain the porter’s five forces model example for the better learning
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