In order, which of these would be the hardest to trek: Amazon forest, Sahara, Himlayas, Greenland, Siberia

If travelled through the heartland of each area, starting from one end to the other, which of these areas would be the hardest to trek through?

  • Amazon Forest
  • Sahara Desert
  • Himlayan Mountains
  • Siberia
  • Greenland

python – Isolation Forest com Validação Cruzada

como posso utilizar um classificador do tipo One Class, como o Isolation Forest, com Validação Cruzada? Estou tentando fazer dessa forma:

columns = data.columns.tolist()
columns = (c for c in columns if c not in ("Class"))
target = "Class"
X = data(columns)
Y = data(target)
Fraud = data(data("Class")==1)
Valid = data(data("Class")==0)
outlier_fraction = 0.5

x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.2) 

 modelIF = IsolationForest(max_samples=100,contamination = outlier_fraction,random_state=1)
    modelIF.fit(X)
    scores_pred=modelIF.decision_function(X)
    y_pred=modelIF.predict(X)
    #0 for valid and 1 for Fraud    
    y_pred(y_pred==1)=0
    y_pred(y_pred==-1)=1
    #metrics without cv
    print(accuracy_score(Y,y_pred))
    print(classification_report(Y,y_pred))

Mas, mesmo seguindo a documentação oficial do sklearn, o resultado final está sendo nulo nan

from sklearn.model_selection import cross_val_score
scores = cross_val_score(modelIF, Y, y_pred, scoring='accuracy', cv=5)
print(scores)

(nan nan nan nan nan)

matrix – Function representing a forest fire after one time step

How can I create a function that would have as its argument an array mat of 0s, 1s, and 2s where the 2s are burning trees and the function would give an array that represents the forest after one time step?

I have tried using

mat=RandomChoice[{0,1,2},{10,10}]
nextstep[mat_]:=Sequence[mat2 + {1, 0}, mat2 + {0, 1}, mat2 + {-1, 0}, mat2 + {0,-1}]

but that doesn’t seem to work. After one time step, every tree, represented by 1s, that is in the von Neumann neighborhood of a burning tree would catch fire, but the 0s, which means there are no trees, would remain the same.

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SMO, Random forest and Bayes net algorithms: why does Random forest perform better?

I analyzed a dataset using those 3 different algorithms.
As I can see, Random forest performs better in most cases.
My dataset is composed of 4000 instances of two classes (class A 2000 elements, class B 2000 elements).
I use 207 metrics to classify the instances, but I also use the first 20 or 10 best metrics for InformationGain.
My question is: why sometimes an algorithm performs better than another one (in this case I’m only comparing this 3).
I read about them but I would like to have a complete scenario of why in some case RF is better than Bayes net and why sometimes is the opposite. And why SMO is always worst than the other two, in my experiences. Thank you so much!

Proof that the following algorithm constructs a Minimum Spanning Forest (MFS)

I have this algorithm pseudocode:

Build-MSF-By-Add-And-Fix (G, w)
1  F = {} // empty set
2  for each edge e ∈ E, taken in arbitrary order
3      F = F ∪ {e}
4      if F has a cycle c
5          let e' be a maximum weight edge on c
6          F = F − {e'}
7  return F

I’m still trying to complete the proof for this but I’m not managing to connect all the dots.
Starting with a counterpart of the safe edge theorem, this is what I have:

Safe edge removal theorem: Given a graph with a single cycle, removing the edge with the maximum weight on that cycle results in a forest with the smallest total weight.

Proof: Since there is only one cycle, to create a forest, we need to remove only one edge from the cycle. Removing any edge that is not the largest from the cycle will result in a forest with a larger total weight. Q.E.D.

Claim: When Build-MSF-By-Add-And-Fix terminates, it will produce a Minimum Spanning Forest.

Proof: By induction on the number of edges inspected of this proposition:

Proposition P(k): “After k edges have been inspected (after k iterations), the graph F is a minimum spanning forest (MSF) of the graph G that contains only the edges inspected in line 2”.

Base k=1:

Induction P(k) => P(k+1) (Assume P(k) is true and prove P(k+1) is true. There will be two cases to consider):

Case 1:

Case 2:

Conclusion: Use the above to conclude that F is correct when the loop terminates and the algorithm returns F:

I think I’m on the right track but I’m stuck trying to figure the remainder out.

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FireHeaven
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[Vn5socks.net] Auto update 24/7 – Good socks 9h40 PM
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