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Brixia Brescia Forum qui "Piazza della Loggia" appellatur Nomina Latina alia: Barixia -ae, Bressa -ae, Brexia -ae, Brexiona -ae, Briscia -ae, Brisia -ae, Brissia -ae, Brixianorum civitas Dialectus brixianus: Brèsa, Brèssa, Brèha Langobardice: Brèsa Langobardo sermone orientali: Bressa, Brèscia Administratio Terra: Italia Regio: Langobardia Provincia: Brixiana Indicia fundamentalia Coordinata: 45° 32′ 0″ Sept. , 10° 14′ 0″ Ort. Altitudo: 150 m supra mare Area: 90 km² Incolae: 196 120 (2015) Spissitudo: 2087 per km² Vici: Vide: Fractiones, vicos et locos in municipio Municipia proxima: Burgus Saturus, Botticinum, Bovetium (alia nomina: Bovecium, Bovezzum), Castrum Novum seu Castrum Novum cum Colorno et Onsato (deinde: Castrum Mellae), Castenedulum seu Castenedolium, Celatica, Collis Beatus (olim: Cobiatum), Concesium (alia nomina: Concesum), Flerum, Gussag...

1 0 $begingroup$ I am trying out isolation forest to detect outliers in a specific target column of my dataset. The dataset contains 188 rows of data with 178 rows with the same value for that target column and the isolation forest gives out every single value -1. Is that a bug or should I take it as that the values are fine? Here is a piece of the code. (I know I need to stop using ix ). import pandas as pd import numpy as np from sklearn.ensemble import IsolationForest df1 = pd.read_csv('C:/Users/smotapar/Desktop/ase/source/data.csv') clf = IsolationForest(n_estimators=200, random_state=10, bootstrap=False) clf.fit(df1.ix[:,"target"].values.reshape(-1, 1)) clf.predict(df1.ix[:,"target"].values.reshape(-1, 1)) Which gives out an output: array([-1, -1, -1, -1, -1, -1, -1...