./readyDOS/ loading

float inhale_rate = 0.5;
float exhale_rate = 1.0;
while(1) { 
    // Inhale phase
    for(float i = 0; i <= 1; i += inhale_rate) {
        printf("\rInhaling... (%f%%)\n", i * 100);
        sleep(1); // Sleep for 1 second to simulate time passing
    }

    // Pause between inhale and exhale
    printf("\rPausing...\n");
    sleep(2);

    // Exhale phase
    for(float i = 1; i >= 0; i -= exhale_rate) {
                    printf("\rExhaling... (%f%%)\n", i * 100);
                    sleep(1); // Sleep for 1 second to simulate time passing
    }

    // Pause between exhale and inhale
    printf("\rPausing...\n");
    sleep(2);
}

double x_min = -2.0;
double y_min = -1.5;
double x_max = 1.0;
double y_max = 1.5;

for (int j = 0; j < height; ++j) {
    for (int i = 0; i < width; ++i) {
        std::complex<double> 
            c((x_min + (x_max - x_min) * i / (width - 1)), 
                ((y_min + (y_max - y_min)) * j) / (height - 1));

        int iter = 0;
        std::complex<double> z(0, 0);

        while (std::abs(z) <= 2 && iter < 255) {
                z = z * z + c;
                ++iter;
        }

        unsigned char color[] = {
            static_cast<unsigned char>(iter % 8 * 32),
            static_cast<unsigned char>(iter % 16 * 17),
            static_cast<unsigned char>(iter % 32 * 14)
        };
        file.write(reinterpret_cast<char*>(color), sizeof(color));
    }
}

using ll = long long;
const ll INF = (1LL<<62);
vector<ll> dist(n, INF);
priority_queue<pair<ll,int>, vector<pair<ll,int>>, greater<pair<ll,int>>> pq;

dist[s] = 0;
pq.push({0, s});
while (!pq.empty()) {
    auto [d, u] = pq.top(); pq.pop();
    if (d != dist[u]) continue;
    for (auto [v, w] : g[u]) {
        if (dist[v] > d + w) {
            dist[v] = d + w;
            pq.push({dist[v], v});
        }
    }
}

struct DSU {
    vector<int> p, r;
    DSU(int n): p(n), r(n,0) { iota(p.begin(), p.end(), 0); }
    int find(int a){ return p[a]==a? a : p[a]=find(p[a]); }
    bool unite(int a,int b){
        a=find(a); b=find(b);
        if(a==b) return false;
        if(r[a]<r[b]) swap(a,b);
        p[b]=a;
        if(r[a]==r[b]) r[a]++;
        return true;
    }
};

vector<int> pi(const string& s){
        int n=s.size();
        vector<int> p(n);
        for(int i=1;i<n;i++){
                int j=p[i-1];
                while(j>0 && s[i]!=s[j]) j=p[j-1];
                if(s[i]==s[j]) j++;
                p[i]=j;
        }
        return p;
}

struct BIT {
        int n; vector<long long> bit;
        BIT(int n): n(n), bit(n+1,0) {}
        void add(int i,long long v){ for(++i;i<=n;i+=i&-i) bit[i]+=v; }
        long long sum(int i){ long long r=0; for(++i;i>0;i-=i&-i) r+=bit[i]; return r; }
};

vector<int> nge(n, -1);
stack<int> st;
for(int i=0;i<n;i++){
        while(!st.empty() && a[st.top()] < a[i]){
                nge[st.top()] = i;
                st.pop();
        }
        st.push(i);
}

queue<int> q;
for(int i=0;i<n;i++) if(indeg[i]==0) q.push(i);
vector<int> order;
while(!q.empty()){
        int u=q.front(); q.pop();
        order.push_back(u);
        for(int v: adj[u]){
                if(--indeg[v]==0) q.push(v);
        }
}

struct Seg {
        int n; vector<long long> t;
        Seg(int n): n(n), t(4*n, INF) {}
        void upd(int v,int tl,int tr,int pos,ll val){
                if(tl==tr){ t[v]=val; return; }
                int tm=(tl+tr)/2;
                if(pos<=tm) upd(v*2,tl,tm,pos,val);
                else upd(v*2+1,tm+1,tr,pos,val);
                t[v]=min(t[v*2],t[v*2+1]);
        }
        ll qry(int v,int tl,int tr,int l,int r){
                if(l>r) return INF;
                if(l==tl && r==tr) return t[v];
                int tm=(tl+tr)/2;
                return min(qry(v*2,tl,tm,l,min(r,tm)),
                                      qry(v*2+1,tm+1,tr,max(l,tm+1),r));
        }
};;
                    
float inhale_rate = 0.5;
float exhale_rate = 1.0;
while(1) { 
    // Inhale phase
    for(float i = 0; i <= 1; i += inhale_rate) {
        printf("\rInhaling... (%f%%)\n", i * 100);
        sleep(1); // Sleep for 1 second to simulate time passing
    }

    // Pause between inhale and exhale
    printf("\rPausing...\n");
    sleep(2);

    // Exhale phase
    for(float i = 1; i >= 0; i -= exhale_rate) {
                    printf("\rExhaling... (%f%%)\n", i * 100);
                    sleep(1); // Sleep for 1 second to simulate time passing
    }

    // Pause between exhale and inhale
    printf("\rPausing...\n");
    sleep(2);
}

double x_min = -2.0;
double y_min = -1.5;
double x_max = 1.0;
double y_max = 1.5;

for (int j = 0; j < height; ++j) {
    for (int i = 0; i < width; ++i) {
        std::complex<double> 
            c((x_min + (x_max - x_min) * i / (width - 1)), 
                ((y_min + (y_max - y_min)) * j) / (height - 1));

        int iter = 0;
        std::complex<double> z(0, 0);

        while (std::abs(z) <= 2 && iter < 255) {
                z = z * z + c;
                ++iter;
        }

        unsigned char color[] = {
            static_cast<unsigned char>(iter % 8 * 32),
            static_cast<unsigned char>(iter % 16 * 17),
            static_cast<unsigned char>(iter % 32 * 14)
        };
        file.write(reinterpret_cast<char*>(color), sizeof(color));
    }
}

using ll = long long;
const ll INF = (1LL<<62);
vector<ll> dist(n, INF);
priority_queue<pair<ll,int>, vector<pair<ll,int>>, greater<pair<ll,int>>> pq;

dist[s] = 0;
pq.push({0, s});
while (!pq.empty()) {
    auto [d, u] = pq.top(); pq.pop();
    if (d != dist[u]) continue;
    for (auto [v, w] : g[u]) {
        if (dist[v] > d + w) {
            dist[v] = d + w;
            pq.push({dist[v], v});
        }
    }
}

struct DSU {
    vector<int> p, r;
    DSU(int n): p(n), r(n,0) { iota(p.begin(), p.end(), 0); }
    int find(int a){ return p[a]==a? a : p[a]=find(p[a]); }
    bool unite(int a,int b){
        a=find(a); b=find(b);
        if(a==b) return false;
        if(r[a]<r[b]) swap(a,b);
        p[b]=a;
        if(r[a]==r[b]) r[a]++;
        return true;
    }
};

vector<int> pi(const string& s){
        int n=s.size();
        vector<int> p(n);
        for(int i=1;i<n;i++){
                int j=p[i-1];
                while(j>0 && s[i]!=s[j]) j=p[j-1];
                if(s[i]==s[j]) j++;
                p[i]=j;
        }
        return p;
}

struct BIT {
        int n; vector<long long> bit;
        BIT(int n): n(n), bit(n+1,0) {}
        void add(int i,long long v){ for(++i;i<=n;i+=i&-i) bit[i]+=v; }
        long long sum(int i){ long long r=0; for(++i;i>0;i-=i&-i) r+=bit[i]; return r; }
};

vector<int> nge(n, -1);
stack<int> st;
for(int i=0;i<n;i++){
        while(!st.empty() && a[st.top()] < a[i]){
                nge[st.top()] = i;
                st.pop();
        }
        st.push(i);
}

queue<int> q;
for(int i=0;i<n;i++) if(indeg[i]==0) q.push(i);
vector<int> order;
while(!q.empty()){
        int u=q.front(); q.pop();
        order.push_back(u);
        for(int v: adj[u]){
                if(--indeg[v]==0) q.push(v);
        }
}

struct Seg {
        int n; vector<long long> t;
        Seg(int n): n(n), t(4*n, INF) {}
        void upd(int v,int tl,int tr,int pos,ll val){
                if(tl==tr){ t[v]=val; return; }
                int tm=(tl+tr)/2;
                if(pos<=tm) upd(v*2,tl,tm,pos,val);
                else upd(v*2+1,tm+1,tr,pos,val);
                t[v]=min(t[v*2],t[v*2+1]);
        }
        ll qry(int v,int tl,int tr,int l,int r){
                if(l>r) return INF;
                if(l==tl && r==tr) return t[v];
                int tm=(tl+tr)/2;
                return min(qry(v*2,tl,tm,l,min(r,tm)),
                                      qry(v*2+1,tm+1,tr,max(l,tm+1),r));
        }
};;
ReadyDOS
  • automationProductskeyboard_arrow_down

Ranked Models

Customer Intelligence

04/17/2026 12:08

run identifier

• b952e960-5a3a-4748-981c-352583bb5ac8

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9392F1 Score0.8633Precision • Recall0.8390 0.8890

Recommendations

04/17/2026 07:10

run identifier

• ebe7be94-a682-4239-839a-763269fe289b

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.999976R.M.S.E • M.A.E • M.S.E.0.0012 0.0000 0.0025

Fraud Detection

04/16/2026 05:43

run identifier

• 707d642e-f72b-4f0c-8486-17308b8c87e4

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9964F1 Score0.9764Precision • Recall1.0000 0.9538
Live Logs

🧬 Loading data ﹙≈ 3-8 mins; standby﹚

run identifier: 82fc7b7...

🌱 Generating stochastic, realistic synthetic users and activity events

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📝 New client data not found

run identifier: 82fc7b7...

⌕ Checking for new training data

run identifier: 82fc7b7...

▶ Starting

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✔ Completed

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💾 Persisting model

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🌢 Persisting metrics

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ƒ(x) Evaluating

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λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

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λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

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λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

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λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

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λ · 🌳 Fast Tree Binary · AUC (PR) 0.93

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λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

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λ · 🌳 Fast Tree Binary · AUC (PR) 0.93

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.93

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.93

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🍃.ೃ Fast Forest Binary · AUC (PR) 0.90

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · 🍃.ೃ Fast Forest Binary · AUC (PR) 0.91

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.90

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.93

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.93

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.93

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · 🍃.ೃ Fast Forest Binary · AUC (PR) 0.90

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🍃.ೃ Fast Forest Binary · AUC (PR) 0.90

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.93

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.93

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.93

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.94

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.91

run identifier: 04efc14...

λ · ⚙️ L-BFGS Logistic Regression Binary · AUC (PR) 0.92

run identifier: 04efc14...

λ · 🍃.ೃ Fast Forest Binary · AUC (PR) 0.91

run identifier: 04efc14...

λ · 🍃.ೃ Fast Forest Binary · AUC (PR) 0.90

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.93

run identifier: 04efc14...

λ · 🍃.ೃ Fast Forest Binary · AUC (PR) 0.91

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.91

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.90

run identifier: 04efc14...

λ · 🍃.ೃ Fast Forest Binary · AUC (PR) 0.91

run identifier: 04efc14...

λ · 🌳 Fast Tree Binary · AUC (PR) 0.90

run identifier: 04efc14...

∈ New Customer Intelligence workflow

run identifier: 04efc14...

⧉ Training

run identifier: 04efc14...

∞ Building estimator chain

run identifier: 04efc14...

⧉ Training

run identifier: 04efc14...

← ▣ → Splitting data

run identifier: 04efc14...

✨ Segmenting

run identifier: 04efc14...

⚡ Data loaded

run identifier: 04efc14...

🧬 Loading data ﹙≈ 3-8 mins; standby﹚

run identifier: 04efc14...

🌱 Generating stochastic, realistic synthetic users and activity events

run identifier: 04efc14...

📝 New client data not found

run identifier: 04efc14...

⌕ Checking for new training data

run identifier: 04efc14...

▶ Starting

run identifier: 04efc14...

Workflow History

04/17/2026 10:04

Customer Intelligence • run identifier • 04efc149-42f6-4008-ae65-ee3c4f163dbd

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9393F1 Score0.8613Precision • Recall0.8309 0.8940

04/17/2026 09:41

Recommendations • run identifier • 5bfde9d6-2f06-4be5-affb-467407b0ff52

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.997466R.M.S.E • M.A.E • M.S.E.0.0179 0.0007 0.0261

04/17/2026 09:25

Customer Intelligence • run identifier • 9af3cc78-5980-42aa-bfa5-e08633315f66

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9356F1 Score0.8590Precision • Recall0.8293 0.8908

04/17/2026 09:08

Fraud Detection • run identifier • 21131706-5083-46ea-bc6f-b1b07adf7fb3

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9965F1 Score0.9738Precision • Recall1.0000 0.9490

04/17/2026 09:01

Recommendations • run identifier • fb4d6ab4-91ad-4e96-bf45-8c9853558384

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.984693R.M.S.E • M.A.E • M.S.E.0.0534 0.0041 0.0644

04/17/2026 08:44

Customer Intelligence • run identifier • d1c07115-536d-4c77-934e-2e8fe4ed01d2

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9385F1 Score0.8581Precision • Recall0.8364 0.8810

04/17/2026 08:25

Fraud Detection • run identifier • c7af7de4-80d6-48c3-b06d-b7af2829c024

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9957F1 Score0.9708Precision • Recall1.0000 0.9432

04/17/2026 08:18

Recommendations • run identifier • 21ad204b-0f86-487e-a698-d6db4e72b240

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.999916R.M.S.E • M.A.E • M.S.E.0.0018 0.0000 0.0048

04/17/2026 08:01

Customer Intelligence • run identifier • fd1d9b21-fc8a-4a71-94da-f815cf76395b

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9380F1 Score0.8604Precision • Recall0.8366 0.8855

04/17/2026 07:34

Customer Intelligence • run identifier • 83b3ed53-3fff-4371-891b-0ca1924f9deb

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9357F1 Score0.8590Precision • Recall0.8375 0.8816

04/17/2026 07:18

Fraud Detection • run identifier • 407f9a7c-3ce0-4fec-b9cf-2b7336eafaa8

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9950F1 Score0.9704Precision • Recall1.0000 0.9425

04/17/2026 07:10

Recommendations • run identifier • ebe7be94-a682-4239-839a-763269fe289b

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.999976R.M.S.E • M.A.E • M.S.E.0.0012 0.0000 0.0025

04/17/2026 06:54

Customer Intelligence • run identifier • 211236ed-1962-4c06-9931-dc4e16c987d4

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9372F1 Score0.8593Precision • Recall0.8341 0.8861

04/17/2026 06:35

Fraud Detection • run identifier • cf0b2d91-2e2d-45ab-a171-f94a3c532df0

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9952F1 Score0.9726Precision • Recall1.0000 0.9466

04/17/2026 06:27

Recommendations • run identifier • cfa58923-4891-4e0d-9f35-6b1f0e0c903c

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.999905R.M.S.E • M.A.E • M.S.E.0.0018 0.0000 0.0051

04/17/2026 06:11

Customer Intelligence • run identifier • 374caf58-80aa-4b4f-9eed-41e619cff917

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9340F1 Score0.8580Precision • Recall0.8353 0.8821

04/17/2026 05:36

Customer Intelligence • run identifier • b271c0bc-e532-4b90-aa4b-b98158936fe2

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9362F1 Score0.8589Precision • Recall0.8296 0.8903

04/17/2026 05:19

Fraud Detection • run identifier • 1fb1b64e-70a6-4239-9319-143c1bc0ae3c

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9959F1 Score0.9756Precision • Recall0.9990 0.9534

04/17/2026 05:12

Recommendations • run identifier • 28222455-e392-468a-84f6-fc3dac5960c9

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.974299R.M.S.E • M.A.E • M.S.E.0.0649 0.0070 0.0837

04/17/2026 04:55

Customer Intelligence • run identifier • 9c0c0bf6-3972-49e8-a79f-376a54bb0dca

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9375F1 Score0.8611Precision • Recall0.8351 0.8887

04/17/2026 04:38

Fraud Detection • run identifier • 93312c5f-ec0d-4301-ac6b-49b3a4f29d28

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9949F1 Score0.9672Precision • Recall1.0000 0.9365

04/17/2026 04:30

Recommendations • run identifier • c09fa592-29c0-427f-8daf-656261781f7f

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.999715R.M.S.E • M.A.E • M.S.E.0.0059 0.0001 0.0088

04/17/2026 04:14

Customer Intelligence • run identifier • 13baee84-9e3c-4895-ab99-e2daebaaeb95

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9359F1 Score0.8570Precision • Recall0.8320 0.8835

04/17/2026 03:57

Fraud Detection • run identifier • 56f7ba88-eadf-4ad7-bc60-fe55abb3726f

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9946F1 Score0.9681Precision • Recall0.9973 0.9406

04/17/2026 03:49

Recommendations • run identifier • 833c93b3-8829-4e34-89d9-dd7eaf0030ba

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.956802R.M.S.E • M.A.E • M.S.E.0.0832 0.0117 0.1084

04/17/2026 03:33

Customer Intelligence • run identifier • e72f5706-2f9d-424e-8e9a-845c59b5c001

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9360F1 Score0.8562Precision • Recall0.8321 0.8817

04/17/2026 03:16

Fraud Detection • run identifier • 564d9a10-b2e8-4c37-8477-877d27e1243c

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9953F1 Score0.9694Precision • Recall0.9947 0.9453

04/17/2026 03:08

Recommendations • run identifier • 20b89e20-86b1-4475-b021-b409aade8d27

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.998439R.M.S.E • M.A.E • M.S.E.0.0137 0.0004 0.0206

04/17/2026 02:52

Customer Intelligence • run identifier • d29675ab-9b08-4525-8bb2-02e106366e2c

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9348F1 Score0.8550Precision • Recall0.8286 0.8832

04/17/2026 02:35

Fraud Detection • run identifier • 87a1d5d6-3980-4064-9b99-5acb55caf8e3

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9964F1 Score0.9742Precision • Recall1.0000 0.9498

04/17/2026 02:27

Recommendations • run identifier • b67a99f6-824a-4f92-ae24-c422ca5b1a52

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.999841R.M.S.E • M.A.E • M.S.E.0.0037 0.0000 0.0066

04/17/2026 02:11

Customer Intelligence • run identifier • a2f43602-7149-460f-a7c9-cd9b06fb7f91

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9353F1 Score0.8587Precision • Recall0.8293 0.8904

04/17/2026 01:54

Fraud Detection • run identifier • 1517279a-bc3d-4b82-a3e5-c3be50b238a5

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9962F1 Score0.9732Precision • Recall0.9968 0.9506

04/17/2026 01:46

Recommendations • run identifier • e04fd1bb-8423-4432-bcf2-3f5251ec93e9

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.989754R.M.S.E • M.A.E • M.S.E.0.0401 0.0028 0.0531

04/17/2026 01:30

Customer Intelligence • run identifier • 5e683f47-8702-4f35-a041-925b1ddc9afa

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9379F1 Score0.8604Precision • Recall0.8413 0.8804

04/17/2026 01:13

Fraud Detection • run identifier • fac2a73a-81d0-45ea-bbe2-0ab8d89c53ae

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9953F1 Score0.9710Precision • Recall1.0000 0.9436

04/17/2026 01:05

Recommendations • run identifier • 15ad7fc3-46da-405e-90d0-649f6de4e4a9

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.999803R.M.S.E • M.A.E • M.S.E.0.0045 0.0001 0.0073

04/17/2026 12:49

Customer Intelligence • run identifier • dfb6fe2e-fe97-4714-a6c9-38be3d2d54a1

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9374F1 Score0.8634Precision • Recall0.8299 0.8996

04/17/2026 12:32

Fraud Detection • run identifier • 70cf53cb-633a-48da-8d77-f54c3ca75be5

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9959F1 Score0.9733Precision • Recall1.0000 0.9480

04/17/2026 12:24

Recommendations • run identifier • 98dd9dc3-44bd-4415-9753-a4f70e4cbc20

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.996754R.M.S.E • M.A.E • M.S.E.0.0200 0.0009 0.0298

04/17/2026 12:08

Customer Intelligence • run identifier • b952e960-5a3a-4748-981c-352583bb5ac8

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9392F1 Score0.8633Precision • Recall0.8390 0.8890

04/16/2026 11:51

Fraud Detection • run identifier • 1c0a6d26-3424-4a9e-a90e-420e9ce42e05

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9954F1 Score0.9675Precision • Recall0.9976 0.9392

04/16/2026 11:44

Recommendations • run identifier • c898ed45-34a1-44af-8fb7-6211aee464f0

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.999883R.M.S.E • M.A.E • M.S.E.0.0020 0.0000 0.0056

04/16/2026 11:27

Customer Intelligence • run identifier • 8d05e78a-f0ba-4062-ba04-f0b5e2bfaa95

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9338F1 Score0.8560Precision • Recall0.8274 0.8866

04/16/2026 11:10

Fraud Detection • run identifier • 19535c55-3cea-42d0-b8f4-3e57f6e787c2

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9956F1 Score0.9743Precision • Recall0.9976 0.9520

04/16/2026 11:02

Recommendations • run identifier • 7335f7c3-13bb-4474-8327-e875780033e8

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.969668R.M.S.E • M.A.E • M.S.E.0.0679 0.0080 0.0897

04/16/2026 10:46

Customer Intelligence • run identifier • d9c0431c-d5a4-4b20-a695-840e9018ad3c

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9342F1 Score0.8595Precision • Recall0.8306 0.8905

04/16/2026 10:29

Fraud Detection • run identifier • b9a58170-99bc-41cd-983e-1e9eba401c2b

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9964F1 Score0.9712Precision • Recall1.0000 0.9440

04/16/2026 10:21

Recommendations • run identifier • 789a0e03-7e0e-4aca-94a2-390eaeddb680

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.999972R.M.S.E • M.A.E • M.S.E.0.0004 0.0000 0.0027

04/16/2026 10:05

Customer Intelligence • run identifier • 35a65afa-ce40-4f54-b6f1-676685212211

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9363F1 Score0.8601Precision • Recall0.8351 0.8867

04/16/2026 09:48

Fraud Detection • run identifier • 81e1a80a-76c1-4fd1-a6f7-c999d3e58da7

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9964F1 Score0.9725Precision • Recall0.9955 0.9506

04/16/2026 09:41

Recommendations • run identifier • 451bf979-49bd-47de-8239-90bf6dcbb80c

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.998663R.M.S.E • M.A.E • M.S.E.0.0131 0.0004 0.0189

04/16/2026 09:24

Customer Intelligence • run identifier • 56aa04ff-99d4-43f0-b335-f019d59bd880

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9368F1 Score0.8596Precision • Recall0.8347 0.8860

04/16/2026 09:07

Fraud Detection • run identifier • 53e4d542-3ed5-4a21-8163-bb0ed950379b

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9960F1 Score0.9709Precision • Recall1.0000 0.9435

04/16/2026 09:00

Recommendations • run identifier • 3fb9faef-b20b-48d9-b356-477aa6ab3e2d

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.995317R.M.S.E • M.A.E • M.S.E.0.0229 0.0012 0.0352

04/16/2026 08:43

Customer Intelligence • run identifier • c0c06d42-e673-4a0c-bcb4-353ef2d20d20

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9370F1 Score0.8576Precision • Recall0.8331 0.8835

04/16/2026 08:26

Fraud Detection • run identifier • d2b3cbb5-a7bb-4511-b3c8-c5eaf82696ca

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9946F1 Score0.9705Precision • Recall0.9975 0.9449

04/16/2026 08:19

Recommendations • run identifier • 8e7399d9-6748-4c6c-ba98-ac0666b1b880

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.999891R.M.S.E • M.A.E • M.S.E.0.0013 0.0000 0.0054

04/16/2026 08:02

Customer Intelligence • run identifier • 67d46ba1-196a-4dab-8349-0a16fab8ea47

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9382F1 Score0.8590Precision • Recall0.8349 0.8845

04/16/2026 07:45

Fraud Detection • run identifier • 347cbb9b-06cb-4c5e-be3a-34005c0f42a0

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9950F1 Score0.9708Precision • Recall1.0000 0.9433

04/16/2026 07:38

Recommendations • run identifier • 5919db70-d3bc-4c69-9141-e9ba01aa4a13

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.999948R.M.S.E • M.A.E • M.S.E.0.0020 0.0000 0.0037

04/16/2026 07:21

Customer Intelligence • run identifier • d9537103-b65b-426f-8498-23cf20f3c735

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9368F1 Score0.8597Precision • Recall0.8323 0.8889

04/16/2026 07:04

Fraud Detection • run identifier • 286f264d-1816-4e12-8ea4-d60fe17fcd9a

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9952F1 Score0.9689Precision • Recall0.9990 0.9406

04/16/2026 06:57

Recommendations • run identifier • 65fa23ed-e286-43d8-8212-3a7e178bdae6

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.994405R.M.S.E • M.A.E • M.S.E.0.0306 0.0015 0.0386

04/16/2026 06:40

Customer Intelligence • run identifier • 6c91357c-6b91-400b-8e63-58efc239330d

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9349F1 Score0.8583Precision • Recall0.8286 0.8902

04/16/2026 06:23

Fraud Detection • run identifier • 0ff9cba8-5ff9-4f09-b753-0fa7052aba35

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9937F1 Score0.9658Precision • Recall0.9968 0.9366

04/16/2026 06:16

Recommendations • run identifier • b4bad328-35ca-4613-9a38-49fe82261ffb

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.990072R.M.S.E • M.A.E • M.S.E.0.0383 0.0026 0.0514

04/16/2026 05:59

Customer Intelligence • run identifier • 4ef1d54d-2dcf-4d33-985b-479eb0605d4d

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9375F1 Score0.8611Precision • Recall0.8378 0.8856

04/16/2026 05:43

Fraud Detection • run identifier • 707d642e-f72b-4f0c-8486-17308b8c87e4

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9964F1 Score0.9764Precision • Recall1.0000 0.9538

04/16/2026 05:35

Recommendations • run identifier • 277537c7-3410-4307-81a9-51bbc18e4bcc

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.998276R.M.S.E • M.A.E • M.S.E.0.0154 0.0005 0.0214

04/16/2026 05:18

Customer Intelligence • run identifier • eec79e2b-99bc-429f-8e91-11cf040d13b1

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9361F1 Score0.8572Precision • Recall0.8304 0.8858

04/16/2026 05:01

Fraud Detection • run identifier • d956387e-63f1-4695-bee3-6958f4234380

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9949F1 Score0.9682Precision • Recall1.0000 0.9383

04/16/2026 04:54

Recommendations • run identifier • c928a8cb-db34-48da-9643-2a5c3d0f6717

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.999880R.M.S.E • M.A.E • M.S.E.0.0034 0.0000 0.0057

04/16/2026 04:37

Customer Intelligence • run identifier • e3c6be0c-7eac-4ae1-a45e-64564cefce5d

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9358F1 Score0.8601Precision • Recall0.8352 0.8866

04/16/2026 04:20

Fraud Detection • run identifier • f81b08ff-f00a-4c3e-8d9c-4088d50a9f9d

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9962F1 Score0.9735Precision • Recall1.0000 0.9483

04/16/2026 04:12

Recommendations • run identifier • d1aa4d62-9c36-4bcd-b602-b598c914f56e

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.950835R.M.S.E • M.A.E • M.S.E.0.0938 0.0132 0.1148

04/16/2026 03:56

Customer Intelligence • run identifier • 8bced0c9-9909-4445-ae7f-18f8ff616f2d

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9308F1 Score0.8587Precision • Recall0.8229 0.8979

04/16/2026 03:39

Fraud Detection • run identifier • d2c9aa80-1c02-4560-9185-e7ca8963f149

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9936F1 Score0.9657Precision • Recall0.9989 0.9346

04/16/2026 03:31

Recommendations • run identifier • 3c881fa1-f8ee-42e6-9251-ae28cad6e6ec

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.999958R.M.S.E • M.A.E • M.S.E.0.0017 0.0000 0.0033

04/16/2026 03:15

Customer Intelligence • run identifier • abd258e7-5aec-4b38-928c-10d9a64ba1e2

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9359F1 Score0.8566Precision • Recall0.8386 0.8753

04/16/2026 02:56

Fraud Detection • run identifier • 69f8abbf-c316-4e78-b3a6-aa2bf23fe666

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9956F1 Score0.9674Precision • Recall1.0000 0.9369

04/16/2026 02:48

Recommendations • run identifier • 35eec795-dce0-4142-a3b0-9049c9d32940

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.980302R.M.S.E • M.A.E • M.S.E.0.0585 0.0052 0.0723

04/16/2026 02:32

Customer Intelligence • run identifier • e95ca2e1-f797-4297-89e5-c128e7cd4d35

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9344F1 Score0.8574Precision • Recall0.8319 0.8846

04/16/2026 01:43

Customer Intelligence • run identifier • f883be09-16a2-4d9e-b1db-7139b44a990f

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9383F1 Score0.8605Precision • Recall0.8345 0.8882

04/16/2026 01:26

Fraud Detection • run identifier • 568f4db3-70bb-4e75-9c99-2a9bbfe120ca

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9949F1 Score0.9697Precision • Recall1.0000 0.9411

04/16/2026 01:18

Recommendations • run identifier • ca4dd199-5b41-4eac-8e62-92e46b087613

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.999914R.M.S.E • M.A.E • M.S.E.0.0018 0.0000 0.0048

04/16/2026 01:02

Customer Intelligence • run identifier • b9531f4f-08e0-43fb-9e27-92b0bed068a5

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9378F1 Score0.8625Precision • Recall0.8391 0.8873

04/16/2026 12:45

Fraud Detection • run identifier • ebcb0a52-2180-4686-b9a6-efef42046c68

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9955F1 Score0.9733Precision • Recall1.0000 0.9479

04/16/2026 12:37

Recommendations • run identifier • 9757d43b-8b6d-4dbd-aa61-cccf99daebdb

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.994029R.M.S.E • M.A.E • M.S.E.0.0290 0.0016 0.0402

04/16/2026 12:21

Customer Intelligence • run identifier • c261d707-31d3-4990-9e07-f866b46c92f1

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9384F1 Score0.8595Precision • Recall0.8336 0.8872

04/16/2026 12:04

Fraud Detection • run identifier • 92508388-da43-496d-88b3-d31e27c9a8ac

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9951F1 Score0.9705Precision • Recall1.0000 0.9426

04/16/2026 11:56

Recommendations • run identifier • 2c64569a-a5fc-41bc-9c97-6b24f7ab758b

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.999916R.M.S.E • M.A.E • M.S.E.0.0020 0.0000 0.0048

04/16/2026 11:40

Customer Intelligence • run identifier • 60bb7091-8161-411d-bd53-8625f53c850c

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9333F1 Score0.8575Precision • Recall0.8273 0.8901

04/16/2026 11:23

Fraud Detection • run identifier • 4d7423ec-d41b-4aaa-b967-1caf771f437b

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9947F1 Score0.9719Precision • Recall1.0000 0.9454

04/16/2026 11:15

Recommendations • run identifier • 6d94c425-b41f-4d3a-93d5-700c9ba9a957

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Forest RegressionR²0.999900R.M.S.E • M.A.E • M.S.E.0.0025 0.0000 0.0052

04/16/2026 10:58

Customer Intelligence • run identifier • 206da4b5-2ce9-474f-a670-0a4d8bec0864

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryK-Means++A.U.C.0.9367F1 Score0.8577Precision • Recall0.8362 0.8803

04/16/2026 10:42

Fraud Detection • run identifier • 99bae545-d01e-4c22-82ca-2fa9862857bd

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Tree BinaryA.U.C.0.9960F1 Score0.9724Precision • Recall1.0000 0.9462

04/16/2026 10:34

Recommendations • run identifier • 121f7b6c-839c-4633-9a03-d5e7540d4d1c

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.999615R.M.S.E • M.A.E • M.S.E.0.0072 0.0001 0.0102

04/16/2026 10:18

Customer Intelligence • run identifier • 36f96625-f855-49ef-ac06-911861d6b390

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryK-Means++A.U.C.0.9338F1 Score0.8553Precision • Recall0.8287 0.8836

04/16/2026 10:01

Fraud Detection • run identifier • 97e82f9d-a84f-420a-ae92-63e4bd481852

Algorithm(s)
A.U.C.
F1 Score
Precision • Recall
Algorithm(s)Fast Forest BinaryA.U.C.0.9949F1 Score0.9720Precision • Recall1.0000 0.9455

04/16/2026 09:53

Recommendations • run identifier • 5c062ce5-b53b-40eb-ad1d-da56d8cf5201

Algorithm(s)
R²
R.M.S.E • M.A.E • M.S.E.
Algorithm(s)Fast Tree RegressionR²0.999632R.M.S.E • M.A.E • M.S.E.0.0058 0.0001 0.0100