learning_curve¶

In [ ]:

learning_curve(estimator,
               input_relation: (str, vDataFrame),
               X: list,
               y: str,
               sizes: list = [0.1, 0.33, 0.55, 0.78, 1.0],
               method = "efficiency",
               metric: str = "auto",
               cv: int = 3,
               pos_label: (int, float, str) = None,
               cutoff: float = -1,
               std_coeff: float = 1,
               ax=None,
               **style_kwds,)

Draws the learning curve.

Parameters¶

Name	Type	Optional	Description
estimator	object	❌	Vertica estimator having a fit method.
input_relation	str / vDataFrame	❌	Input Relation.
X	list	❌	List of the predictor columns.
y	str	❌	Response Column.
sizes	list	✓	Different sizes of the dataset used to train the model. Multiple models will be trained using the different sizes.
metric	str / list	✓	Method used to plot the curve. efficiency : draws train/test score vs sample size. performance : draws score vs time. scalability : draws time vs sample size.
metric	str / list	✓	Metric used to do the model evaluation. It can also be a list of metrics. auto : logloss for classification & rmse for regression. For Classification: accuracy : Accuracy auc : Area Under the Curve (ROC) bm : Informedness = tpr + tnr - 1 csi : Critical Success Index = tp / (tp + fn + fp) f1 : F1 Score logloss : Log Loss mcc : Matthews Correlation Coefficient mk : Markedness = ppv + npv - 1 npv : Negative Predictive Value = tn / (tn + fn) prc_auc : Area Under the Curve (PRC) precision : Precision = tp / (tp + fp) recall : Recall = tp / (tp + fn) specificity : Specificity = tn / (tn + fp) For Regression: max : Max Error mae : Mean Absolute Error median : Median Absolute Error mse : Mean Squared Error msle : Mean Squared Log Error r2 : R-squared coefficient r2a : R2 adjusted rmse : Root Mean Squared Error var : Explained Variance
cv	int	✓	Number of folds.
pos_label	int / float / str	✓	The main class to be considered as positive (classification only).
cutoff	float	✓	The model cutoff (classification only).
std_coeff	float	✓	Value of the standard deviation coefficient used to compute the area plot around each score.
ax	Matplotlib axes object	✓	The axes to plot on.
**style_kwds	any	✓	Any optional parameter to pass to the Matplotlib functions.

Returns¶

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example¶

In [14]:

from verticapy.learn.ensemble import RandomForestClassifier
model = RandomForestClassifier(name = "public.RF_titanic")

from verticapy.learn.model_selection import learning_curve

# Efficiency
learning_curve(model,
               input_relation = "public.titanic", 
               X = ["age", "fare", "parch",],
               y = "survived",
               method = "efficiency",
               cv = 3,
               metric = "auc",)

Out[14]:

	n	auc	auc_std	auc_train	auc_train_std	time	time_std
1	118	0.6232071899835059	0.07716828662138914	0.9143753120202394	0.02634265573333186	1.230307976404826	0.08847785026785102
2	440	0.6011753697827655	0.06046211063111672	0.7933566989832863	0.026963249266741856	1.2517418066660564	0.22754656359291583
3	646	0.6855332232705719	0.02492348019764075	0.8048112737377822	0.004788964996400323	1.2833991050720215	0.09871047554219624
4	968	0.6861296016131232	0.031136764230758365	0.7571311455209785	0.010563197482381968	1.545785665512085	0.25085902623688866
5	1234	0.7013934714334608	0.018849921944687584	0.7661496794496074	0.009455102672168694	1.3281141916910808	0.35271516602760966

Rows: 1-5 | Columns: 7

In [15]:

# Scalability
learning_curve(model,
               input_relation = "public.titanic", 
               X = ["age", "fare", "parch",],
               y = "survived",
               method = "scalability",
               cv = 3,
               metric = "auc",)

Out[15]:

	n	auc	auc_std	auc_train	auc_train_std	time	time_std
1	134	0.5167660117660119	0.10353077864547716	0.9054840003508337	0.01634752048085983	1.1386996110280354	0.0705586899199437
2	414	0.6189723320633486	0.048556132519650234	0.8492784185425034	0.011506065970162608	1.2157390117645264	0.24663237448222033
3	661	0.6432733696654005	0.03647230481346208	0.801333493295949	0.004309475247013425	1.2128020127614338	0.26779752805591356
4	951	0.7008182091912799	0.03819265824433921	0.7746292732245985	0.016649369427228198	1.3791486422220867	0.08692612536226947
5	1234	0.7156485855152414	0.016664077986893316	0.7548012522866065	0.006637398330680515	1.8580233256022136	0.4899776062646076

Rows: 1-5 | Columns: 7

In [16]:

# Performance
learning_curve(model,
               input_relation = "public.titanic", 
               X = ["age", "fare", "parch",],
               y = "survived",
               method = "performance",
               cv = 3,
               metric = "auc",)

Out[16]:

	n	auc	auc_std	auc_train	auc_train_std	time	time_std
1	365	0.6301412768226045	0.005093431175974369	0.8556098769808446	0.024814675808506704	1.061522642771403	0.051973754398303214
2	1234	0.692527174591594	0.03672960575642859	0.7746555801007609	0.014991545728900444	1.2669154008229573	0.12237853722391252
3	700	0.7134033218822601	0.018261390100039167	0.7802188043733609	0.003983696171970722	1.3858860333760579	0.6278487950308989
4	121	0.7311534462269755	0.053828273705672246	0.9567689495780072	0.012348668802499468	1.4544970194498699	0.38701140468343154
5	947	0.6664574589587015	0.038781843074524515	0.7997131491013364	0.01798883929383095	1.469599962234497	0.1608715625898985

Rows: 1-5 | Columns: 7