Top Tweets for #sklearn_evaluation
With #sklearn_evaluation, you can generate a classification report easily! It integrates numerical scores, as well as a color-coded heatmap to show the precision, recall, F1, and support scores for the #model. π₯Έ
Generate yours:
π https://t.co/tgIR002Bem
#DataScience #ML
Generate your calibration curves with only a few steps! π€
With #sklearn_evaluation you can generate calibration curves easily, which helps you determine whether you can interpret predicted probabilities as confidence levels.
Plot yours:
π https://t.co/HuYfS7Hi3w
#DataScience
Evaluation reports are an important skill for data scientists. With #sklearn_evaluation you'll be able to create them in a snap! π€
Learn how to create your own reports:
π https://t.co/uj2m0RedfU
#DataScience #OpenSource #Ploomber
A ROC curve is used to evaluate and compare the performance of a binary classifier system. #sklearn_evaluation contains tools to generate a ROC curve easily!
Generate tour ROC curves and evaluate your models easily:
π https://t.co/m7P68pQaYt
#DataScience #ML #Ploomber
#sklearn_evaluation: The easiest way to evaluate your #ML models.
Don't believe us? Try it yourself.
π https://t.co/l66svjcVwP
All about classification! π½π
We prepared a quickstart guide on #classification using #sklearn_evaluation, which includes: data loading, data cleaning, model fitting, features evaluation, and model comparison. It includes code samples!
Check it out!
π https://t.co/WYibYWTXOE
Check out this quick tip to plot the K-S statistic the EASY way! β‘οΈ
The KolmogorovβSmirnov statistic quantifies the distance between the empirical distribution functions of two samples.
Learn to plot it easily with #sklearn_evaluation:
π https://t.co/5CJ8K393Vt
#DataScience
Good #DataScientists know how to measure classifiers!
A lift curve determines how effective a predictive #model is. The greater the area between the lift curve and the baseline, the better the model.
Plot a lift curve easily with #sklearn_evaluation:
π https://t.co/4UCbmkWVmw
Can I trust my modelβs probabilities? π€
A #calibration_curve is a graphical representation of a modelβs calibration. It allows us to benchmark our model against a target: a perfectly calibrated model.
Evaluate your calibration with #sklearn_evaluation:
https://t.co/LsBurScxJn
#sklearn_evaluation allows you to evaluate and compare your #scikit_learn models with multiple plots with a few lines of code, lightening your workload as a data scientist. π
sklearn-evaluation: The simplest way to evaluate your models.
Check it out: https://t.co/NmopXR03AE
#CumulativeGains & #LiftCharts are tools for measuring model performance. Both consist of a curve and a baseline. The greater the area between the curve and the baseline, the better the model.
Take a look at them in #sklearn_evaluation's plot module:
π https://t.co/CFLuAMjPjk
CLUSTERING QUICKSTART! π
We prepared a quickstart guide on #clustering using #sklearn_evaluation, which includes:
πΉ Creating models
πΉ Visualizing clusters
πΉ Evaluation metrics
πΉ Optimal number of clusters
Check it out here!
π https://t.co/FeAlrCuZYd
#DataScience #ML
5 steps to build a cumulative gains curve! π
The cumulative gains chart is used to determine the effectiveness of a binary classifier. These charts are very easy to build with #sklearn_evaluation, check out the link below!
π https://t.co/T7rpiSbp5z
#DataScience #ML
Measure your binary classifiers. π
A lift curve is used to determine how effective a predictive #model is. The greater the area between the lift curve and the baseline, the better the model. π
Plot a lift curve easily with #sklearn_evaluation:
π https://t.co/lsQwlHDUZW
Algo bien cool que tiene #sklearn_evaluation es que tiene funciones que complementan a #sklearn para evaluar tus modelos de buena manera. π₯
Literal una lΓnea de cΓ³digo, Β‘y bum! π₯
Evaluating #MachineLearning models is an essential skill every aspiring data scientist should master. One effective way is by using plots, which allow you to better understand your models. β‘οΈ
Learn to evaluate your models with #sklearn_evaluation:
π https://t.co/geybTxL79s
Evaluating #MachineLearning models is an essential skill every aspiring data scientist should master. One effective way is by using plots, which allow you to better understand your models. β‘οΈ
Learn to evaluate your models with #sklearn_evaluation:
π https://t.co/geybTxL79s
CLASSIFICATION QUICKSTART! π
We prepared a quickstart guide on classification using #sklearn_evaluation, which includes:
πΉ Data loading
πΉ Data cleaning
πΉ Fitting models
πΉ Evaluating features and models
πΉ Comparing models
Check it out here!
π https://t.co/kOwpvM1XEZ
Our amazing tool, sklearn-evaluation, will lighten your workload as a data scientist. Use its tools to get the most out of your model evaluation in a simple way.
#sklearn_evaluation, the simplest way to evaluate your #sklearn models. π
Get started π https://t.co/GeW5XGsXri
Plot the KS statistic with only a few lines of code! π
The KolmogorovβSmirnov statistic quantifies the distance between the empirical distribution functions of two samples.
Plot it easily with #sklearn_evaluation:
π https://t.co/G7gcOmoICf
#MachineLearning #DataScience
The Cook's distance with only a few lines of code! ποΈ
Cook's distance shows the influence of each observation on the response, which is useful for identifying outliers in the X values.
Learn to plot it with #sklearn_evaluation!
π https://t.co/hxf2VzRq8Q
#DataScience #ML
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